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Eldawlatly S. On the role of generative artificial intelligence in the development of brain-computer interfaces. BMC Biomed Eng 2024; 6:4. [PMID: 38698495 PMCID: PMC11064240 DOI: 10.1186/s42490-024-00080-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 04/24/2024] [Indexed: 05/05/2024] Open
Abstract
Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held promise to compensate for functions lost by people with disabilities through allowing direct communication between the brain and external devices. While research throughout the past decades has demonstrated the feasibility of BCI to act as a successful assistive technology, the widespread use of BCI outside the lab is still beyond reach. This can be attributed to a number of challenges that need to be addressed for BCI to be of practical use including limited data availability, limited temporal and spatial resolutions of brain signals recorded non-invasively and inter-subject variability. In addition, for a very long time, BCI development has been mainly confined to specific simple brain patterns, while developing other BCI applications relying on complex brain patterns has been proven infeasible. Generative Artificial Intelligence (GAI) has recently emerged as an artificial intelligence domain in which trained models can be used to generate new data with properties resembling that of available data. Given the enhancements observed in other domains that possess similar challenges to BCI development, GAI has been recently employed in a multitude of BCI development applications to generate synthetic brain activity; thereby, augmenting the recorded brain activity. Here, a brief review of the recent adoption of GAI techniques to overcome the aforementioned BCI challenges is provided demonstrating the enhancements achieved using GAI techniques in augmenting limited EEG data, enhancing the spatiotemporal resolution of recorded EEG data, enhancing cross-subject performance of BCI systems and implementing end-to-end BCI applications. GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.
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Affiliation(s)
- Seif Eldawlatly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt.
- Computer Science and Engineering Department, The American University in Cairo, Cairo, Egypt.
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Padfield N, Agius Anastasi A, Camilleri T, Fabri S, Bugeja M, Camilleri K. BCI-controlled wheelchairs: end-users' perceptions, needs, and expectations, an interview-based study. Disabil Rehabil Assist Technol 2024; 19:1539-1551. [PMID: 37166297 DOI: 10.1080/17483107.2023.2211602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/03/2023] [Indexed: 05/12/2023]
Abstract
PURPOSE Brain-computer interface (BCI)-controlled wheelchairs have the potential to improve the independence of people with mobility impairments. The low uptake of BCI devices has been linked to a lack of knowledge among researchers of the needs of end-users that should influence BCI development. MATERIALS AND METHODS This study used semi-structured interviews to learn about the perceptions, needs, and expectations of spinal cord injury (SCI) patients with regards to a BCI-controlled wheelchair. Topics discussed in the interview include: paradigms, shared control, safety, robustness, channel selection, hardware, and experimental design. The interviews were recorded and then transcribed. Analysis was carried out using coding based on grounded theory principles. RESULTS The majority of participants had a positive view of BCI-controlled wheelchair technology and were willing to use the technology. Core issues were raised regarding safety, cost and aesthetics. Interview discussions were linked to state-of-the-art BCI technology. The results challenge the current reliance of researchers on the motor-imagery paradigm by suggesting end-users expect highly intuitive paradigms. There also needs to be a stronger focus on obstacle avoidance and safety features in BCI wheelchairs. Finally, the development of control approaches that can be personalized for individual users may be instrumental for widespread adoption of these devices. CONCLUSIONS This study, based on interviews with SCI patients, indicates that BCI-controlled wheelchairs are a promising assistive technology that would be well received by end-users. Recommendations for a more person-centered design of BCI controlled wheelchairs are made and clear avenues for future research are identified.
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Affiliation(s)
- Natasha Padfield
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
| | | | - Tracey Camilleri
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
| | - Simon Fabri
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
| | - Marvin Bugeja
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
| | - Kenneth Camilleri
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
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Deng H, Li M, Li J, Guo M, Xu G. A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding. J Neurosci Methods 2024; 405:110108. [PMID: 38458260 DOI: 10.1016/j.jneumeth.2024.110108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters. NEW METHODS This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects. RESULTS The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust. CONCLUSIONS The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.
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Affiliation(s)
- Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China.
| | - Jundi Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Miaomiao Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
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Armocida D, Garbossa D, Cofano F. Letter: Ethical concerns and scientific communication on neuralink device. Neurosurg Rev 2024; 47:194. [PMID: 38664240 DOI: 10.1007/s10143-024-02432-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 04/17/2024] [Accepted: 04/22/2024] [Indexed: 05/09/2024]
Affiliation(s)
- Daniele Armocida
- Neuroscience Department "Rita Levi Montalcini", Neurosurgery Unit, Università degli studi di Torino, Via Cherasco 15, Turin, 10126, TO, Italy.
- Experimental neurosurgery unit, IRCCS "Neuromed", Via Atinense 18, Pozzilli, 86077, IS, Italy.
| | - Diego Garbossa
- Neuroscience Department "Rita Levi Montalcini", Neurosurgery Unit, Università degli studi di Torino, Via Cherasco 15, Turin, 10126, TO, Italy
| | - Fabio Cofano
- Neuroscience Department "Rita Levi Montalcini", Neurosurgery Unit, Università degli studi di Torino, Via Cherasco 15, Turin, 10126, TO, Italy
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Li X, Wang D, Zhang B, Fan C, Chen J, Xu M, Chen Y. [A review on electroencephalogram based channel selection]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2024; 41:398-405. [PMID: 38686423 PMCID: PMC11058489 DOI: 10.7507/1001-5515.202308034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.
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Affiliation(s)
- Xiangzhe Li
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Dan Wang
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Baiwen Zhang
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing 100089, P.R. China
| | - Chaojie Fan
- School of Transportation Engineering, Central South University, Changsha 410075, P.R. China
| | - Jiaming Chen
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Meng Xu
- Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - Yuanfang Chen
- Beijing Institute of Mechanical Equipment, Beijing 100854, P.R. China
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Keough JR, Irvine B, Kelly D, Wrightson J, Comaduran Marquez D, Kinney-Lang E, Kirton A. Fatigue in children using motor imagery and P300 brain-computer interfaces. J Neuroeng Rehabil 2024; 21:61. [PMID: 38658998 PMCID: PMC11040843 DOI: 10.1186/s12984-024-01349-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children. METHODS Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum. RESULTS Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power. CONCLUSION Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.
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Affiliation(s)
- Joanna Rg Keough
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Brian Irvine
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Dion Kelly
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - James Wrightson
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Comaduran Marquez
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Eli Kinney-Lang
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Departments of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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Waisberg E, Ong J, Lee AG. Ethical Considerations of Neuralink and Brain-Computer Interfaces. Ann Biomed Eng 2024:10.1007/s10439-024-03511-2. [PMID: 38602573 DOI: 10.1007/s10439-024-03511-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.
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Affiliation(s)
- Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, UK.
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, USA
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Texas A&M College of Medicine, Bryan, TX, USA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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8
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Klein E, Kinsella M, Stevens I, Fried-Oken M. Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease. Disabil Rehabil Assist Technol 2024; 19:1041-1051. [PMID: 36403143 PMCID: PMC10351684 DOI: 10.1080/17483107.2022.2146217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 09/01/2022] [Accepted: 11/07/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies. METHODS Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory. RESULTS Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual's identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies. CONCLUSIONS With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation.IMPLICATIONS FOR REHABILITATIONIndividuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies.The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy.People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities.Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.
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Affiliation(s)
- Eran Klein
- Department of Neurology, Oregon Health & Science University, Portland, OR USA
| | - Michelle Kinsella
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR USA
| | - Ian Stevens
- Department of Neurosurgery, Oregon Health & Science University, Portland, OR USA
| | - Melanie Fried-Oken
- Department of Neurology, Oregon Health & Science University, Portland, OR USA
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR USA
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Ng HW, Guan C. Subject-independent meta-learning framework towards optimal training of EEG-based classifiers. Neural Netw 2024; 172:106108. [PMID: 38219680 DOI: 10.1016/j.neunet.2024.106108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/13/2023] [Accepted: 01/05/2024] [Indexed: 01/16/2024]
Abstract
Advances in deep learning have shown great promise towards the application of performing high-accuracy Electroencephalography (EEG) signal classification in a variety of tasks. However, many EEG-based datasets are often plagued by the issue of high inter-subject signal variability. Robust deep learning models are notoriously difficult to train under such scenarios, often leading to subpar or widely varying performance across subjects under the leave-one-subject-out paradigm. Recently, the model agnostic meta-learning framework was introduced as a way to increase the model's ability to generalize towards new tasks. While the original framework focused on task-based meta-learning, this research aims to show that the meta-learning methodology can be modified towards subject-based signal classification while maintaining the same task objectives and achieve state-of-the-art performance. Namely, we propose the novel implementation of a few/zero-shot subject-independent meta-learning framework towards multi-class inner speech and binary class motor imagery classification. Compared to current subject-adaptive methods which utilize large number of labels from the target, the proposed framework shows its effectiveness in training zero-calibration and few-shot models for subject-independent EEG classification. The proposed few/zero-shot subject-independent meta-learning mechanism performs well on both small and large datasets and achieves robust, generalized performance across subjects. The results obtained shows a significant improvement over the current state-of-the-art, with the binary class motor imagery achieving 88.70% and the accuracy of multi-class inner speech achieving an average of 31.15%. Codes will be made available to public upon publication.
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Affiliation(s)
- Han Wei Ng
- Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore; AI Singapore, 3 Research Link, 117602, Singapore.
| | - Cuntai Guan
- Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
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Saway BF, Palmer C, Hughes C, Triano M, Suresh RE, Gilmore J, George M, Kautz SA, Rowland NC. The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces. Neurotherapeutics 2024; 21:e00337. [PMID: 38377638 PMCID: PMC11103214 DOI: 10.1016/j.neurot.2024.e00337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 02/22/2024] Open
Abstract
Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.
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Affiliation(s)
- Brian F Saway
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA.
| | - Charles Palmer
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA
| | - Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Matthew Triano
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA
| | - Rishishankar E Suresh
- College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jordon Gilmore
- Department of Bioengineering, Clemson University, Clemson, SC 29634, USA
| | - Mark George
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Steven A Kautz
- Department of Health Science and Research, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Nathan C Rowland
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA; MUSC Institute for Neuroscience Discovery (MIND), Medical University of South Carolina, SC 29425, USA
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11
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Liu X, Hu B, Si Y, Wang Q. The role of eye movement signals in non-invasive brain-computer interface typing system. Med Biol Eng Comput 2024:10.1007/s11517-024-03070-7. [PMID: 38509350 DOI: 10.1007/s11517-024-03070-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 03/05/2024] [Indexed: 03/22/2024]
Abstract
Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.
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Affiliation(s)
- Xi Liu
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China
| | - Bingliang Hu
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China
| | - Yang Si
- Department of Neurology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Chengdu, 611731, China
- University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China.
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China.
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Zhang X, Zhang T, Jiang Y, Zhang W, Lu Z, Wang Y, Tao Q. A novel brain-controlled prosthetic hand method integrating AR-SSVEP augmentation, asynchronous control, and machine vision assistance. Heliyon 2024; 10:e26521. [PMID: 38463871 PMCID: PMC10920167 DOI: 10.1016/j.heliyon.2024.e26521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 11/27/2023] [Accepted: 02/14/2024] [Indexed: 03/12/2024] Open
Abstract
Background and objective The brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) is expected to help disabled patients achieve alternative prosthetic hand assistance. However, the existing study still has some shortcomings in interaction aspects such as stimulus paradigm and control logic. The purpose of this study is to innovate the visual stimulus paradigm and asynchronous decoding/control strategy by integrating augmented reality technology, and propose an asynchronous pattern recognition algorithm, thereby improving the interaction logic and practical application capabilities of the prosthetic hand with the BCI system. Methods An asynchronous visual stimulus paradigm based on an augmented reality (AR) interface was proposed in this paper, in which there were 8 control modes, including Grasp, Put down, Pinch, Point, Fist, Palm push, Hold pen, and Initial. According to the attentional orienting characteristics of the paradigm, a novel asynchronous pattern recognition algorithm that combines center extended canonical correlation analysis and support vector machine (Center-ECCA-SVM) was proposed. Then, this study proposed an intelligent BCI system switch based on a deep learning object detection algorithm (YOLOv4) to improve the level of user interaction. Finally, two experiments were designed to test the performance of the brain-controlled prosthetic hand system and its practical performance in real scenarios. Results Under the AR paradigm of this study, compared with the liquid crystal display (LCD) paradigm, the average SSVEP spectrum amplitude of multiple subjects increased by 17.41%, and the signal-noise ratio (SNR) increased by 3.52%. The average stimulus pattern recognition accuracy was 96.71 ± 3.91%, which was 2.62% higher than the LCD paradigm. Under the data analysis time of 2s, the Center-ECCA-SVM classifier obtained 94.66 ± 3.87% and 97.40 ± 2.78% asynchronous pattern recognition accuracy under the Normal metric and the Tolerant metric, respectively. And the YOLOv4-tiny model achieves a speed of 25.29fps and a 96.4% confidence in the prosthetic hand in real-time detection. Finally, the brain-controlled prosthetic hand helped the subjects to complete 4 kinds of daily life tasks in the real scene, and the time-consuming were all within an acceptable range, which verified the effectiveness and practicability of the system. Conclusion This research is based on improving the user interaction level of the prosthetic hand with the BCI system, and has made improvements in the SSVEP paradigm, asynchronous pattern recognition, interaction, and control logic. Furthermore, it also provides support for BCI areas for alternative prosthetic control, and movement disorder rehabilitation programs.
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Affiliation(s)
- Xiaodong Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an, Shannxi, 710049, China
| | - Teng Zhang
- Zhejiang Normal University, Jinhua, Zhejiang, 321004, China
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an, Shannxi, 710049, China
| | - Yongyu Jiang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China
| | - Weiming Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China
| | - Zhufeng Lu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China
| | - Yu Wang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shannxi, 710049, China
| | - Qing Tao
- School of Mechanical Engineering, Xinjiang University, Wulumuqi, Xinjiang, 830000, China
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Li M, Li J, Song Z, Deng H, Xu J, Xu G, Liao W. EEGNet-based multi-source domain filter for BCI transfer learning. Med Biol Eng Comput 2024; 62:675-686. [PMID: 37982955 DOI: 10.1007/s11517-023-02967-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/06/2023] [Indexed: 11/21/2023]
Abstract
Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.
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Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China.
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China.
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China.
| | - Jundi Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Zhiyong Song
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Jiaming Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Wenzhe Liao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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Mahrooz MH, Fattahzadeh F, Gharibzadeh S. Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback. Appl Psychophysiol Biofeedback 2024; 49:47-53. [PMID: 37540396 DOI: 10.1007/s10484-023-09601-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 08/05/2023]
Abstract
Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.
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Affiliation(s)
- Mohammad H Mahrooz
- Shahid Beheshti Medical University, Tehran, Iran.
- Department of aerospace engineering, Sharif University of Technology, Tehran, Iran.
| | | | - Shahriar Gharibzadeh
- Institue for cognitive and brain sciences, Shahid Beheshti University, Tehran, Iran
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15
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Zou R, Zhao L, He S, Zhou X, Yin X. Effect of the period of EEG signals on the decoding of motor information. Phys Eng Sci Med 2024; 47:249-260. [PMID: 38150057 DOI: 10.1007/s13246-023-01361-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 11/22/2023] [Indexed: 12/28/2023]
Abstract
Decoding movement information from electroencephalogram to construct brain-computer interface has promising applications. The EEG data during the entire motor imagery (MI) period or movement execution (ME) period is generally decoded, and calculation of numerous information and massive dataset is time-consuming. In order to improve decoding efficiency, the joint topographic maps of the brain activation state of 15 subjects were studied during different periods. The results showed that the activation intensity of the preparation period in the motor imagery experiment was higher than during the exercise period, while during the exercise period, the activation intensity was higher than in the preparation period in the movement execution experiment. Hence, the wavelet neural network was used to decode the six-class movements including elbow flexion/extension, forearm pronation/supination and hand open/close in periods of MI/ME. The experimental results show that the accuracy obtained in the preparation period is the highest in the motor imagery experiment, which is 80.77%. On the other hand, the highest accuracy obtained in the exercise period of the movement execution experiment is 79.26%. It further proves that the optimized period is a key decoding factor to reduce the cost of calculation, and this new decoding method is effective to build a more intelligent brain-computer interface system.
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Affiliation(s)
- Renling Zou
- Department of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
| | - Liang Zhao
- Department of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Shuang He
- Department of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaobo Zhou
- Department of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xuezhi Yin
- Shanghai Berry Electronic Technology Co., Ltd, Shanghai, China
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16
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Sengupta P, Lakshminarayanan K. Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery. Behav Brain Res 2024; 459:114760. [PMID: 37979923 DOI: 10.1016/j.bbr.2023.114760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.
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Affiliation(s)
- Puja Sengupta
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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Moaveninejad S, D'Onofrio V, Tecchio F, Ferracuti F, Iarlori S, Monteriù A, Porcaro C. Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface. Comput Methods Programs Biomed 2024; 244:107944. [PMID: 38064955 DOI: 10.1016/j.cmpb.2023.107944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/31/2023] [Accepted: 11/24/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. METHODS In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). RESULTS Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. CONCLUSIONS These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.
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Affiliation(s)
| | | | - Franca Tecchio
- Institute of Cognitive Sciences and Technologies (ISCT) - National Research Council (CNR), 00185 Rome, Italy
| | - Francesco Ferracuti
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Sabrina Iarlori
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Andrea Monteriù
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Camillo Porcaro
- Department of Neuroscience, University of Padova, 35128 Padua, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padua, Italy; Institute of Cognitive Sciences and Technologies (ISCT) - National Research Council (CNR), 00185 Rome, Italy; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK.
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18
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Kucukler OF, Amira A, Malekmohamadi H. EEG dataset for energy data visualizations. Data Brief 2024; 52:109933. [PMID: 38125371 PMCID: PMC10733112 DOI: 10.1016/j.dib.2023.109933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/20/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
User behavior plays a substantial role in shaping household energy use. Nevertheless, the methodologies employed by researchers to examine user behavior exhibit certain limitations in terms of their reach. The present article introduces an openly accessible collection of electroencephalography (EEG) recordings, comprising EEG data collected from individuals who were subjected to energy data visualizations. The dataset comprises EEG recordings obtained from 28 individuals who were in good health. The EEG recordings were collected using a 32-channel EMOTIV EEG device, and the international 10-20 electrode system was employed for precise electrode placement. The energy data visualizations were generated and showcased utilizing the PsychoPy software. To ascertain the participants' affective state, they were requested to rate the valence and arousal of each stimulus through the utilization of a self-assessment manikin (SAM). Additionally, three inquiries were posed for every stimulation. The dataset includes both original data visualizations and ratings. Additionally, the raw EEG data has been divided into segments consisting of data visualizations and neutral images, with the use of event markers, in order to assist analysis. The EEG recordings were recorded and stored utilizing the EMOTIVPro application, whereas the subjective reactions were captured and preserved using the PsychoPy application. Furthermore, the generation of synthetic EEG data is accomplished by employing the Generative Adversarial Network (GAN) architecture on the acquired EEG dataset. The synthetic EEG data created is integrated with empirical EEG data, and afterwards subjected to qualitative and quantitative analysis in order to improve performance. The dataset presented herein showcases a pioneering utilization of EEG investigation and offers a valuable foundation for scholars in the domains of computer science, energy conservation, artificial intelligence, brain-computer interfaces, and human-computer interaction.
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Affiliation(s)
| | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, Leicester, UK
- Department of Computer Science, University of Sharjah, Sharjah, UAE
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19
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Ille N, Nakao Y, Yano S, Taura T, Ebert A, Bornfleth H, Asagi S, Kozawa K, Itabashi I, Sato T, Sakuraba R, Tsuda R, Kakisaka Y, Jin K, Nakasato N. Ongoing EEG artifact correction using blind source separation. Clin Neurophysiol 2024; 158:149-158. [PMID: 38219404 DOI: 10.1016/j.clinph.2023.12.133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/23/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVE Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. METHODS The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. RESULTS The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. CONCLUSIONS Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. SIGNIFICANCE The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.
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Affiliation(s)
| | | | | | | | | | | | - Suguru Asagi
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Kanoko Kozawa
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Izumi Itabashi
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Takafumi Sato
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Rie Sakuraba
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Rie Tsuda
- Clinical Physiological Center, Tohoku University Hospital, Sendai, Japan
| | - Yosuke Kakisaka
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Qin Y, Li B, Wang W, Shi X, Wang H, Wang X. ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network. Brain Res 2024; 1823:148673. [PMID: 37956749 DOI: 10.1016/j.brainres.2023.148673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/16/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Abstract
Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.
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Affiliation(s)
- Yuxin Qin
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Baojiang Li
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China.
| | - Wenlong Wang
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Xingbin Shi
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Haiyan Wang
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Xichao Wang
- The School of Electrical Engineering, Shanghai Dianji University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
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Zhu L, Xu M, Zhu J, Huang A, Zhang J. A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs. Comput Methods Biomech Biomed Engin 2024:1-14. [PMID: 38193151 DOI: 10.1080/10255842.2023.2301421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/24/2023] [Indexed: 01/10/2024]
Abstract
Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial's data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.
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Affiliation(s)
- Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Mengxuan Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jieping Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Aiai Huang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
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Luo R, Mai X, Meng J. Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification. J Neurosci Methods 2024; 401:109982. [PMID: 37839711 DOI: 10.1016/j.jneumeth.2023.109982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/11/2023] [Accepted: 10/11/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback. NEW METHOD In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes. RESULTS The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance. COMPARISON WITH EXISTING METHODS Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder. CONCLUSION The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.
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Affiliation(s)
- Ruijie Luo
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ximing Mai
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jianjun Meng
- Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.
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Chen A, Hao S, Han Y, Fang Y, Miao Y. Exploring the effects of different BCI-based attention training games on the brain: A functional near-infrared spectroscopy study. Neurosci Lett 2024; 818:137567. [PMID: 38007085 DOI: 10.1016/j.neulet.2023.137567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 11/27/2023]
Abstract
BCI games have been widely employed as non-invasive therapeutic interventions for conditions, but their efficacy remains a subject of debate. This study explores the efficacy of two prevalent forms of Brain-Computer Interface (BCI)-based attention training games: video games (VG) and physical games (PG). The effectiveness of these games has been examined through the lens of neuroscience, using functional Near-Infrared Spectroscopy (fNIRS) to monitor cortical activation. After the fNIRS test, subjects completed an Intrinsic Motivation Inventory (IMI) questionnaire. PG tasks activated six channels (L-PFC, R-PFC and R-TL), while VG tasks activated only one (R-PFC). Furthermore, females exhibited stronger activation during PG tasks, while males had none in either. Our findings suggest that under equivalent game rules and themes, PG may prove more effective for cognitive rehabilitation than VG, with stronger intrinsic motivation. We also found this result may exhibit gender differences. Finally, this research offers valuable insights for the future design of BCI-based games from a neuroscience perspective.
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Affiliation(s)
- An Chen
- School of Mechanical Engineering, Shandong University, Jinan 250061, China.
| | - Song Hao
- School of Mechanical Engineering, Shandong University, Jinan 250061, China.
| | - Yongpeng Han
- Edinburgh College of Art, The University of Edinburgh, Edinburgh EH3 9DF, UK.
| | - Yang Fang
- School of Mechanical Engineering, Shandong University, Jinan 250061, China.
| | - Yibei Miao
- School of Mechanical Engineering, Shandong University, Jinan 250061, China.
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Jang H, Park JS, Jun SC, Ahn S. TSANet: multibranch attention deep neural network for classifying tactile selective attention in brain-computer interfaces. Biomed Eng Lett 2024; 14:45-55. [PMID: 38186945 PMCID: PMC10770016 DOI: 10.1007/s13534-023-00309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/22/2023] [Accepted: 07/31/2023] [Indexed: 01/09/2024] Open
Abstract
Brain-computer interfaces (BCIs) enable communication between the brain and a computer and electroencephalography (EEG) has been widely used to implement BCIs because of its high temporal resolution and noninvasiveness. Recently, a tactile-based EEG task was introduced to overcome the current limitations of visual-based tasks, such as visual fatigue from sustained attention. However, the classification performance of tactile-based BCIs as control signals is unsatisfactory. Therefore, a novel classification approach is required for this purpose. Here, we propose TSANet, a deep neural network, that uses multibranch convolutional neural networks and a feature-attention mechanism to classify tactile selective attention (TSA) in a tactile-based BCI system. We tested TSANet under three evaluation conditions, namely, within-subject, leave-one-out, and cross-subject. We found that TSANet achieved the highest classification performance compared with conventional deep neural network models under all evaluation conditions. Additionally, we show that TSANet extracts reasonable features for TSA by investigating the weights of spatial filters. Our results demonstrate that TSANet has the potential to be used as an efficient end-to-end learning approach in tactile-based BCIs. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00309-4.
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Affiliation(s)
- Hyeonjin Jang
- School of Electronic and Electrical Engineering, Kyungpook National University, IT1-505, 80 Daehak-ro, Buk-gu, Daegu, 41566 South Korea
| | - Jae Seong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sung Chan Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Sangtae Ahn
- School of Electronic and Electrical Engineering, Kyungpook National University, IT1-505, 80 Daehak-ro, Buk-gu, Daegu, 41566 South Korea
- School of Electronics Engineering, Kyungpook National University, Daegu, South Korea
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25
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Várkuti B, Halász L, Hagh Gooie S, Miklós G, Smits Serena R, van Elswijk G, McIntyre CC, Lempka SF, Lozano AM, Erōss L. Conversion of a medical implant into a versatile computer-brain interface. Brain Stimul 2024; 17:39-48. [PMID: 38145752 DOI: 10.1016/j.brs.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications. OBJECTIVE Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain. METHODS In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task. RESULTS We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels. CONCLUSION The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.
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Affiliation(s)
| | - László Halász
- Albert-Szentgyörgyi Medical School, Doctoral School of Clinical Medicine, Clinical and Experimental Research for Reconstructive and Organ-Sparing Surgery, University of Szeged, Szeged, Hungary
| | | | - Gabriella Miklós
- CereGate GmbH, München, Germany; National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Ricardo Smits Serena
- CereGate GmbH, München, Germany; Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
| | | | - Cameron C McIntyre
- Department of Biomedical Engineering and Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Scott F Lempka
- Department of Biomedical Engineering, Department of Anesthesiology and the Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Loránd Erōss
- National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
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26
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孙 静, 孟 佳, 尤 佳, 杨 明, 江 京, 许 敏, 明 东. [Research progress of brain-computer interface application paradigms based on rapid serial visual presentation]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:1235-1241. [PMID: 38151948 PMCID: PMC10753308 DOI: 10.7507/1001-5515.202305061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 11/08/2023] [Indexed: 12/29/2023]
Abstract
Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.
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Affiliation(s)
- 静敏 孙
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 佳圆 孟
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 佳 尤
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 明明 杨
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 京 江
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 敏鹏 许
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 东 明
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
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27
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费 克, 蔡 霄, 陈 顺, 潘 礼, 王 炜. [Recognition of motor imagery electroencephalogram based on flicker noise spectroscopy and weighted filter bank common spatial pattern]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:1126-1134. [PMID: 38151935 PMCID: PMC10753307 DOI: 10.7507/1001-5515.202302020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 10/17/2023] [Indexed: 12/29/2023]
Abstract
Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
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Affiliation(s)
- 克玲 费
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 霄仙 蔡
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 顺芝 陈
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 礼正 潘
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 炜 王
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
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Al-Qaysi ZT, Albahri AS, Ahmed MA, Mohammed SM. Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery. Phys Eng Sci Med 2023; 46:1519-1534. [PMID: 37603133 DOI: 10.1007/s13246-023-01316-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.
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Affiliation(s)
- Z T Al-Qaysi
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - A S Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
| | - M A Ahmed
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - Saleh Mahdi Mohammed
- Department of Computer Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
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29
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Suzuki Y, Jovanovic LI, Fadli RA, Yamanouchi Y, Marquez-Chin C, Popovic MR, Nomura T, Milosevic M. Evidence That Brain-Controlled Functional Electrical Stimulation Could Elicit Targeted Corticospinal Facilitation of Hand Muscles in Healthy Young Adults. Neuromodulation 2023; 26:1612-1621. [PMID: 35088740 DOI: 10.1016/j.neurom.2021.12.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/12/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVES Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) has been used in rehabilitation for improving hand motor function. However, mechanisms of improvements are still not well understood. The objective of this study was to investigate how BCI-controlled FES affects hand muscle corticospinal excitability. MATERIALS AND METHODS A total of 12 healthy young adults were recruited in the study. During BCI calibration, a single electroencephalography channel from the motor cortex and a frequency band were chosen to detect event-related desynchronization (ERD) of cortical oscillatory activity during kinesthetic wrist motor imagery (MI). The MI-based BCI system was used to detect active states on the basis of ERD activity in real time and produce contralateral wrist extension movements through FES of the extensor carpi radialis (ECR) muscle. As a control condition, FES was used to generate wrist extension at random intervals. The two interventions were performed on separate days and lasted 25 minutes. Motor evoked potentials (MEPs) in ECR (intervention target) and flexor carpi radialis (FCR) muscles were elicited through single-pulse transcranial magnetic stimulation of the motor cortex to compare corticospinal excitability before (pre), immediately after (post0), and 30 minutes after (post30) the interventions. RESULTS After the BCI-FES intervention, ECR muscle MEPs were significantly facilitated at post0 and post30 time points compared with before the intervention (pre), whereas there were no changes in the FCR muscle corticospinal excitability. Conversely, after the random FES intervention, both ECR and FCR muscle MEPs were unaffected compared with before the intervention (pre). CONCLUSIONS Our results demonstrated evidence that BCI-FES intervention could elicit muscle-specific short-term corticospinal excitability facilitation of the intervention targeted (ECR) muscle only, whereas randomly applied FES was ineffective in eliciting any changes. Notably, these findings suggest that associative cortical and peripheral activations during BCI-FES can effectively elicit targeted muscle corticospinal excitability facilitation, implying possible rehabilitation mechanisms.
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Affiliation(s)
- Yoshiyuki Suzuki
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, Toyonaka, Osaka, Japan
| | - Lazar I Jovanovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Rizaldi A Fadli
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, Toyonaka, Osaka, Japan
| | - Yuki Yamanouchi
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, Toyonaka, Osaka, Japan
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada; CRANIA, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada; CRANIA, University Health Network and University of Toronto, Toronto, Ontario, Canada
| | - Taishin Nomura
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, Toyonaka, Osaka, Japan
| | - Matija Milosevic
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, Toyonaka, Osaka, Japan.
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30
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Hao Z, Zhai X, Peng B, Cheng D, Zhang Y, Pan Y, Dou W. CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG. Neuroimage 2023; 282:120405. [PMID: 37820859 DOI: 10.1016/j.neuroimage.2023.120405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/19/2023] [Accepted: 10/08/2023] [Indexed: 10/13/2023] Open
Abstract
Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.
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Affiliation(s)
- Zexuan Hao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Xiaoxue Zhai
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Bo Peng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Dandan Cheng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yanlin Zhang
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yu Pan
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China.
| | - Weibei Dou
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
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31
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Okahara Y, Takano K, Odaka K, Uchino Y, Kansaku K. Detecting passive and active response in patients with behaviourally diagnosed unresponsive wakefulness syndrome. Neurosci Res 2023; 196:23-31. [PMID: 37302715 DOI: 10.1016/j.neures.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 05/29/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The diagnosis of unresponsive wakefulness syndrome depends mostly on the motor response following verbal commands. However, there is a potential for misdiagnosis in patients who understand verbal commands (passive response) but cannot perform voluntary movements (active response). To evaluate passive and active responses in such patients, this study used an approach combining functional magnetic resonance imaging and passive listening tasks to evaluate the level of speech comprehension, with portable brain-computer interface modalities that were applied to elicit an active response to attentional modulation tasks at the bedside. We included ten patients who were clinically diagnosed as unresponsive wakefulness syndrome. Two of ten patients showed no significant activation, while limited activation in the auditory cortex was found in six patients. The remaining two patients showed significant activation in language areas, and were able to control the brain-computer interface with reliable accuracy. Using a combined passive/active approach, we identified unresponsive wakefulness syndrome patients who showed both active and passive neural responses. This suggests that some patients with unresponsive wakefulness syndrome diagnosed behaviourally are both wakeful and responsive, and the combined approach is useful for distinguishing a minimally conscious state from unresponsive wakefulness syndrome physiologically.
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Affiliation(s)
- Yoji Okahara
- Department of Neurological Surgery, Chiba Cerebral and Cardiovascular Center, Chiba, Japan; Systems Neuroscience Section, Department of Rehabilitation for Brain Functions, Research Institute of National Rehabilitation for Persons with Disabilities, Saitama, Japan
| | - Kouji Takano
- Systems Neuroscience Section, Department of Rehabilitation for Brain Functions, Research Institute of National Rehabilitation for Persons with Disabilities, Saitama, Japan
| | | | | | - Kenji Kansaku
- Systems Neuroscience Section, Department of Rehabilitation for Brain Functions, Research Institute of National Rehabilitation for Persons with Disabilities, Saitama, Japan; Department of Physiology, Dokkyo Medical University School of Medicine, Tochigi, Japan; Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, Japan.
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32
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Leinders S, Vansteensel MJ, Piantoni G, Branco MP, Freudenburg ZV, Gebbink TA, Pels EGM, Raemaekers MAH, Schippers A, Aarnoutse EJ, Ramsey NF. Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome. Clin Neurophysiol 2023; 155:1-15. [PMID: 37657190 DOI: 10.1016/j.clinph.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/29/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome. METHODS Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively). RESULTS Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95 Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control. CONCLUSIONS Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates. SIGNIFICANCE If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.
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Affiliation(s)
- Sacha Leinders
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Giovanni Piantoni
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mariana P Branco
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Zac V Freudenburg
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Tineke A Gebbink
- Department of Clinical Neurophysiology, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Elmar G M Pels
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mathijs A H Raemaekers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Anouck Schippers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands.
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Selvam A, Aggarwal T, Mukherjee M, Verma YK. Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0. Biotechnol Adv 2023; 68:108237. [PMID: 37604228 DOI: 10.1016/j.biotechadv.2023.108237] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/15/2023] [Accepted: 08/18/2023] [Indexed: 08/23/2023]
Abstract
The evolution of industries have introduced versatile technologies, motivating limitless possibilities of tackling pivotal global predicaments in the arenas of medicine, environment, defence, and national security. In this direction, ardently emerges the new era of Industry 5.0 through the eyes of biomanufacturing, which integrates the most advanced systems 21st century has to offer by means of integrating artificial systems to mimic and nativize the natural milieu to substitute the deficits of nature, thence leading to a new meta world. Albeit, it questions the natural order of the living world, which necessitates certain paramount stipulations to be addressed for a successful expansion of biomanufacturing Industry 5.0. Can humans live in synergism with artificial beings? How can humans establish dominance of hierarchy with artificial counterparts? This perspective provides a bird's eye view on the plausible direction of a new meta world inquisitively. For this purpose, we propose the influence of internet of things (IoT) via new generation interfacial systems, such as, human-machine interface (HMI) and brain-computer interface (BCI) in the domain of tissue engineering and regenerative medicine, which can be extended to target modern warfare and smart healthcare.
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Affiliation(s)
- Abhyavartin Selvam
- Amity Institute of Nanotechnology, Amity University Noida, Uttar Pradesh 201303, India
| | - Tanishka Aggarwal
- Department of Biotechnology, School of Chemical and Life Sciences (SCLS) Jamia Hamdard, New Delhi 110062, India
| | - Monalisa Mukherjee
- Amity Institute of Click Chemistry Research and Studies, Amity University Noida, Uttar Pradesh 201303, India
| | - Yogesh Kumar Verma
- Stem Cell & Tissue Engineering Research Group, Institute of Nuclear Medicine and Allied Sciences, Defence Research and Development Organisation, New Delhi 110054, India.
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Chen K, Garcia Padilla C, Kiselyov K, Kozai TDY. Cell-specific alterations in autophagy-lysosomal activity near the chronically implanted microelectrodes. Biomaterials 2023; 302:122316. [PMID: 37738741 PMCID: PMC10897938 DOI: 10.1016/j.biomaterials.2023.122316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/22/2023] [Accepted: 09/02/2023] [Indexed: 09/24/2023]
Abstract
Intracortical microelectrodes that can record and stimulate brain activity have become a valuable technique for basic science research and clinical applications. However, long-term implantation of these microelectrodes can lead to progressive neurodegeneration in the surrounding microenvironment, characterized by elevation in disease-associated markers. Dysregulation of autophagy-lysosomal degradation, a major intracellular waste removal process, is considered a key factor in the onset and progression of neurodegenerative diseases. It is plausible that similar dysfunctions in autophagy-lysosomal degradation contribute to tissue degeneration following implantation-induced focal brain injury, ultimately impacting recording performance. To understand how the focal, persistent brain injury caused by long-term microelectrode implantation impairs autophagy-lysosomal pathway, we employed two-photon microscopy and immunohistology. This investigation focused on the spatiotemporal characterization of autophagy-lysosomal activity near the chronically implanted microelectrode. We observed an aberrant accumulation of immature autophagy vesicles near the microelectrode over the chronic implantation period. Additionally, we found deficits in autophagy-lysosomal clearance proximal to the chronic implant, which was associated with an accumulation of autophagy cargo and a reduction in lysosomal protease level during the chronic period. Furthermore, our evidence demonstrates reactive astrocytes have myelin-containing lysosomes near the microelectrode, suggesting its role of myelin engulfment during acute implantation period. Together, this study sheds light on the process of brain tissue degeneration caused by long-term microelectrode implantation, with a specific focus on impaired intracellular waste degradation.
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Affiliation(s)
- Keying Chen
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Camila Garcia Padilla
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Kirill Kiselyov
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Takashi D Y Kozai
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA; NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
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35
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Obukhov NV, Naish PLN, Solnyshkina IE, Siourdaki TG, Martynov IA. Real-time assessment of hypnotic depth, using an EEG-based brain-computer interface: a preliminary study. BMC Res Notes 2023; 16:288. [PMID: 37875937 PMCID: PMC10599062 DOI: 10.1186/s13104-023-06553-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/02/2023] [Indexed: 10/26/2023] Open
Abstract
OBJECTIVE Hypnosis can be an effective treatment for many conditions, and there have been attempts to develop instrumental approaches to continuously monitor hypnotic state level ("depth"). However, there is no method that addresses the individual variability of electrophysiological hypnotic correlates. We explore the possibility of using an EEG-based passive brain-computer interface (pBCI) for real-time, individualised estimation of the hypnosis deepening process. RESULTS The wakefulness and deep hypnosis intervals were manually defined and labelled in 27 electroencephalographic (EEG) recordings obtained from eight outpatients after hypnosis sessions. Spectral analysis showed that EEG correlates of deep hypnosis were relatively stable in each patient throughout the treatment but varied between patients. Data from each first session was used to train classification models to continuously assess deep hypnosis probability in subsequent sessions. Models trained using four frequency bands (1.5-45, 1.5-8, 1.5-14, and 4-15 Hz) showed accuracy mostly exceeding 85% in a 10-fold cross-validation. Real-time classification accuracy was also acceptable, so at least one of the four bands yielded results exceeding 74% in any session. The best results averaged across all sessions were obtained using 1.5-14 and 4-15 Hz, with an accuracy of 82%. The revealed issues are also discussed.
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Affiliation(s)
- Nikita V Obukhov
- Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation.
- Department of Psychotherapy, Academician I.P. Pavlov First St. Petersburg State Medical University, 6-8, L. Tolstoy str, Saint Petersburg, 197022, Russian Federation.
| | - Peter L N Naish
- Department of Psychology, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK
| | - Irina E Solnyshkina
- Department of Psychotherapy, Academician I.P. Pavlov First St. Petersburg State Medical University, 6-8, L. Tolstoy str, Saint Petersburg, 197022, Russian Federation
| | - Tatiana G Siourdaki
- Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation
| | - Ilya A Martynov
- Research Department, The Association of Experts in the Field of Clinical Hypnosis, 40, Kamennoostrovsky Ave., 410, Saint Petersburg, 197022, Russian Federation
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Chen K, Cambi F, Kozai TDY. Pro-myelinating clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health. Biomaterials 2023; 301:122210. [PMID: 37413842 PMCID: PMC10528716 DOI: 10.1016/j.biomaterials.2023.122210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023]
Abstract
Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over the chronic implantation period. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.
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Affiliation(s)
- Keying Chen
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Franca Cambi
- Veterans Administration Pittsburgh, Pittsburgh, PA, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Takashi D Y Kozai
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA; NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
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Zhang R, Liu G, Wen Y, Zhou W. Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification. J Neurosci Methods 2023; 398:109953. [PMID: 37611877 DOI: 10.1016/j.jneumeth.2023.109953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 07/20/2023] [Accepted: 08/19/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Motor imagery (MI) based brain-computer interfaces (BCIs) have promising potentials in the field of neuro-rehabilitation. However, due to individual variations in active brain regions during MI tasks, the challenge of decoding MI EEG signals necessitates improved classification performance for practical application. NEW METHOD This study proposes a self-attention-based Convolutional Neural Network (CNN) in conjunction with a time-frequency common spatial pattern (TFCSP) for enhanced MI classification. Due to the limited availability of training data, a data augmentation strategy is employed to expand the scale of MI EEG datasets. The self-attention-based CNN is trained to automatically extract the temporal and spatial information from EEG signals, allowing the self-attention module to select active channels by calculating EEG channel weights. TFCSP is further implemented to extract multiscale time-frequency-space features from EEG data. Finally, the EEG features derived from TFCSP are concatenated with those from the self-attention-based CNN for MI classification. RESULTS The proposed method is evaluated on two publicly accessible datasets, BCI Competition IV IIa and BCI Competition III IIIa, yielding mean accuracies of 79.28 % and 86.39 %, respectively. CONCLUSIONS Compared with state-of-the-art methods, our approach achieves superior classification results in accuracy. Self-attention-based CNN combining with TFCSP can make full use of the time-frequency-space information of EEG, and enhance the classification performance.
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Affiliation(s)
- Rui Zhang
- School of Microelectronics, Shandong University, Jinan 250100, China
| | - Guoyang Liu
- School of Microelectronics, Shandong University, Jinan 250100, China
| | - Yiming Wen
- School of Microelectronics, Shandong University, Jinan 250100, China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, China.
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38
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Zhang L, Li C, Zhang R, Sun Q. Online semi-supervised learning for motor imagery EEG classification. Comput Biol Med 2023; 165:107405. [PMID: 37678137 DOI: 10.1016/j.compbiomed.2023.107405] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/29/2023] [Accepted: 08/26/2023] [Indexed: 09/09/2023]
Abstract
OBJECTIVE Time-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to integrate readily available, as well as informative, unlabeled data online, whereas this approach is less investigated. APPROACH We proposed an online semi-supervised learning scheme to improve the classification performance of MI-based BCI. This scheme uses regularized weighted online sequential extreme learning machine (RWOS-ELM) as the base classifier and updates its model parameters with incoming balanced data chunk-by-chunk. In the initial stage, we designed a technique that combines the synthetic minority oversampling with the edited nearest neighbor rule for data augmentation to construct more discriminative initial classifiers. When used online, the incoming chunk of data is first pseudo-labeled by RWOS-ELM as well as an auxiliary classifier, and then balanced again by the above-mentioned technique. Initial classifiers are further updated based on these class-balanced data. MAIN RESULTS Offline experimental results on two publicly available MI datasets demonstrate the superiority of the proposed scheme over its counterparts. Further online experiments on six subjects show that their BCI performance gradually improved by learning from incoming unlabeled data. SIGNIFICANCE Our proposed online semi-supervised learning scheme has higher computation and memory usage efficiency, which is promising for online MI-based BCIs, especially in the case of insufficient labeled training data.
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Affiliation(s)
- Li Zhang
- State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China.
| | - Changsheng Li
- State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China
| | - Run Zhang
- Marketing Service Center, State Grid Chongqing Electric Power Company, Yuzhong District, Chongqing, 400014, People's Republic of China
| | - Qiang Sun
- State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China
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Abstract
Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.
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Affiliation(s)
- Evelyn Karikari
- Department of Public Health and Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Konstantin A Koshechkin
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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罗 睿, 豆 心, 肖 晓, 吴 乔, 许 敏, 明 东. [Recognition of high-frequency steady-state visual evoked potential for brain-computer interface]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:683-691. [PMID: 37666758 PMCID: PMC10477378 DOI: 10.7507/1001-5515.202302034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/05/2023] [Indexed: 09/06/2023]
Abstract
Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.
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Affiliation(s)
- 睿心 罗
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 心怡 豆
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 晓琳 肖
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 乔逸 吴
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 敏鹏 许
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 东 明
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
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李 奇, 张 庭, 宋 雨, 刘 玉, 孙 美. [A design and evaluation of wearable p300 brain-computer interface system based on Hololens2]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:709-717. [PMID: 37666761 PMCID: PMC10477399 DOI: 10.7507/1001-5515.202207055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 06/06/2023] [Indexed: 09/06/2023]
Abstract
Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.
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Affiliation(s)
- 奇 李
- 长春理工大学 计算机科学技术学院(长春 130022)School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, P. R. China
- 长春理工大学中山研究院(广东中山 528437)Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, Guangdong 528437, P. R. China
| | - 庭嘉 张
- 长春理工大学 计算机科学技术学院(长春 130022)School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, P. R. China
| | - 雨 宋
- 长春理工大学 计算机科学技术学院(长春 130022)School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, P. R. China
| | - 玉龙 刘
- 长春理工大学 计算机科学技术学院(长春 130022)School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, P. R. China
| | - 美琪 孙
- 长春理工大学 计算机科学技术学院(长春 130022)School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, P. R. China
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赵 薇, 徐 立, 肖 晓, 奕 伟, 陈 远, 王 坤, 许 敏, 明 东. [Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:409-417. [PMID: 37380378 PMCID: PMC10307616 DOI: 10.7507/1001-5515.202208047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 04/14/2023] [Indexed: 06/30/2023]
Abstract
High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.
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Affiliation(s)
- 薇 赵
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 立超 徐
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 晓琳 肖
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 伟波 奕
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 远方 陈
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - 坤 王
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 敏鹏 许
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - 东 明
- 天津大学 精密仪器与光电子工程学院(天津 300072)School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
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李 红, 刘 浩, 陈 虹, 张 荣. [Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:418-425. [PMID: 37380379 PMCID: PMC10307594 DOI: 10.7507/1001-5515.202205069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 03/31/2023] [Indexed: 06/30/2023]
Abstract
The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.
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Affiliation(s)
- 红利 李
- 天津工业大学 控制科学与工程学院(天津 300387)School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China
| | - 浩雨 刘
- 天津工业大学 控制科学与工程学院(天津 300387)School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China
| | - 虹宇 陈
- 天津工业大学 控制科学与工程学院(天津 300387)School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China
| | - 荣华 张
- 天津工业大学 控制科学与工程学院(天津 300387)School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China
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Liu X, Zhang W, Li W, Zhang S, Lv P, Yin Y. Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial. BMC Neurol 2023; 23:136. [PMID: 37003976 PMCID: PMC10064693 DOI: 10.1186/s12883-023-03150-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/07/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Seeking positive and comprehensive rehabilitation methods after stroke is an urgent problem to be solved, which is very important to improve the dysfunction of stroke. The aim of this study was to investigate the effects of motor imagery-based brain-computer interface training (MI-BCI) on upper limb function and attention in stroke patients with hemiplegia. METHODS Sixty stroke patients with impairment of upper extremity function and decreased attention were randomly assigned to the control group (CR group) or the experimental group (BCI group) in a 1:1 ratio. Patients in the CR group received conventional rehabilitation. Patients in the BCI group received 20 min of MI-BCI training five times a week for 3 weeks (15 sessions) in addition to conventional rehabilitation. The primary outcome measures were the changes in Fugl-Meyer Motor Function Assessment of Upper Extremities (FMA-UE) and Attention Network Test (ANT) from baseline to 3 weeks. RESULTS About 93% of the patients completed the allocated training. Compared with the CR group, among those in the BCI group, FMA-UE was increased by 8.0 points (95%CI, 5.0 to 10.0; P < 0.001). Alert network response time (32.4ms; 95%CI, 58.4 to 85.6; P < 0.001), orienting network response (5.6ms; 95%CI, 29.8 to 55.8; P = 0.010), and corrects number (8.0; 95%CI, 17.0 to 28.0; P < 0.001) also increased in the BCI group compared with the CR group. Additionally, the executive control network response time (- 105.9ms; 95%CI, - 68.3 to - 23.6; P = 0.002), the total average response time (- 244.8ms; 95%CI, - 155.8 to - 66.2; P = 0.002), and total time (- 122.0ms; 95%CI, - 80.0 to - 35.0; P = 0.001) were reduced in the BCI group compared with the CR group. CONCLUSION MI-BCI combined with conventional rehabilitation training could better enhance upper limb motor function and attention in stroke patients. This training method may be feasible and suitable for individuals with stroke. TRIAL REGISTRATION This study was registered in the Chinese Clinical Trial Registry with Portal Number ChiCTR2100050430(27/08/2021).
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Affiliation(s)
- Xiaolu Liu
- College of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan, 063210, China
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive Disorders, Shijiazhuang, 050000, China
| | - Wendong Zhang
- Department of Rehabilitation, Hebei General Hospital, Shijiazhuang, 050000, China
| | - Weibo Li
- Department of Gastrointestinal Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
| | - Shaohua Zhang
- Department of Medical, The Eighth People's Hospital of Hebei Province, Shijiazhuang, 050000, China
| | - Peiyuan Lv
- Department of Neurology, Hebei General Hospital, Shijiazhuang, 050000, China
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive Disorders, Shijiazhuang, 050000, China
| | - Yu Yin
- Department of Rehabilitation, Hebei General Hospital, Shijiazhuang, 050000, China.
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive Disorders, Shijiazhuang, 050000, China.
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Dos Santos EM, San-Martin R, Fraga FJ. Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers. Med Biol Eng Comput 2023; 61:835-845. [PMID: 36626112 DOI: 10.1007/s11517-023-02769-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023]
Abstract
Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.
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46
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Zheng C, Liu Y, Xiao X, Zhou X, Xu F, Xu M, Ming D. [Advances in brain-computer interface based on high-frequency steady-state visual evoked potential]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:155-162. [PMID: 36854561 DOI: 10.7507/1001-5515.202205090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.
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Affiliation(s)
- Chenguang Zheng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.,School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072, P. R. China
| | - Yang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China
| | - Xiaolin Xiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.,School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - Xiaoyu Zhou
- School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - Fangzhou Xu
- School of Electronic and Information Engineering, Qilu University of Technology, Jinan 250353, P. R. China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.,School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.,School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China
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Kern K, Vukelić M, Guggenberger R, Gharabaghi A. Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients. Neuroimage Clin 2023; 37:103289. [PMID: 36525745 PMCID: PMC9791174 DOI: 10.1016/j.nicl.2022.103289] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/03/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.
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Affiliation(s)
- Kevin Kern
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany
| | - Mathias Vukelić
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany
| | - Robert Guggenberger
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany
| | - Alireza Gharabaghi
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany.
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Hu Y, Liu Y, Cheng C, Geng C, Dai B, Peng B, Zhu J, Dai Y. [Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39:1065-1073. [PMID: 36575074 DOI: 10.7507/1001-5515.202206052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.
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Affiliation(s)
- Ying Hu
- School of Biomedical Engineering, University of Science and Technology of China, Hefei 230026, P. R. China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China.,Suzhou Guoke Kangcheng Medical Technology Co., LTD, Suzhou, Jiangsu 215163, P. R. China
| | - Chenchen Cheng
- School of Automation, Harbin University of Science and Technology, Harbin 150080, P. R. China
| | - Chen Geng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - Bin Dai
- School of Biomedical Engineering, University of Science and Technology of China, Hefei 230026, P. R. China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu 215163, P. R. China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China.,Suzhou Guoke Medical Technology Development (Group) Co., LTD, Suzhou, Jiangsu 215163, P. R. China.,Suzhou Guoke Kangcheng Medical Technology Co., LTD, Suzhou, Jiangsu 215163, P. R. China
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王 浩, 王 绍, 邱 兆, 张 琦, 许 帅. [Design and preliminary application of outdoor flying pigeon-robot]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39:1209-1217. [PMID: 36575091 PMCID: PMC9927186 DOI: 10.7507/1001-5515.202207077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/31/2022] [Indexed: 12/29/2022]
Abstract
Control at beyond-visual ranges is of great significance to animal-robots with wide range motion capability. For pigeon-robots, such control can be done by the way of onboard preprogram, but not constitute a closed-loop yet. This study designed a new control system for pigeon-robots, which integrated the function of trajectory monitoring to that of brain stimulation. It achieved the closed-loop control in turning or circling by estimating pigeons' flight state instantaneously and the corresponding logical regulation. The stimulation targets located at the formation reticularis medialis mesencephali (FRM) in the left and right brain, for the purposes of left- and right-turn control, respectively. The stimulus was characterized by the waveform mimicking the nerve cell membrane potential, and was activated intermittently. The wearable control unit weighted 11.8 g totally. The results showed a 90% success rate by the closed-loop control in pigeon-robots. It was convenient to obtain the wing shape during flight maneuver, by equipping a pigeon-robot with a vivo camera. It was also feasible to regulate the evolution of pigeon flocks by the pigeon-robots at different hierarchical level. All of these lay the groundwork for the application of pigeon-robots in scientific researches.
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Affiliation(s)
- 浩 王
- 南京航空航天大学 机电学院(南京 210016)College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
- 山东省科学院 生物研究所(济南 250353)Biology Institute, Shandong Academy of Sciences, Jinan 250353, P. R. China
| | - 绍康 王
- 南京航空航天大学 机电学院(南京 210016)College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
| | - 兆成 邱
- 南京航空航天大学 机电学院(南京 210016)College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
| | - 琦 张
- 南京航空航天大学 机电学院(南京 210016)College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
| | - 帅 许
- 南京航空航天大学 机电学院(南京 210016)College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
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50
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潘 礼, 丁 忆, 王 顺, 宋 爱. [Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39:1173-1180. [PMID: 36575087 PMCID: PMC9927178 DOI: 10.7507/1001-5515.202112023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 09/26/2022] [Indexed: 12/29/2022]
Abstract
Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
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Affiliation(s)
- 礼正 潘
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
- 东南大学 远程测控技术江苏省重点实验室(南京 210096)Provincial Key Laboratory of Remote Measurement and Control Technology, Southeast University, Nanjing 210096, P.R. China
| | - 忆 丁
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 顺超 王
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
| | - 爱国 宋
- 常州大学 机械与轨道交通学院(江苏常州 213164)School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China
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