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Zhou W, Wu L, Gao Y, Chen X. A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2114-2123. [PMID: 38829754 DOI: 10.1109/tnsre.2024.3408273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.
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2
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Pitt KM, Spoor A, Zosky J. Considering preferences, speed and the animation of multiple symbols in developing P300 brain-computer interface for children. Disabil Rehabil Assist Technol 2024:1-13. [PMID: 38808372 DOI: 10.1080/17483107.2024.2359479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/20/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE Prior research has begun establishing the efficacy of animation in brain-computer interfaces access to augmentative and alternative communication (BCI-AAC). However, the use of animation in P300-BCI-AAC for children is in the early stages and largely limited to single item highlighting of extended durations. In pursuit of practical application, the present study aims to evaluate children's event-related potential (ERP) characteristics and user experience during a task involving an animated P300-BCI-AAC system. MATERIALS AND METHODS The system utilizes multi-item zoom animations to access a 28-pictorial symbols. Participants completed a fast (100 ms) and slow (200 ms) zoom animation highlighting conditions wherein four pictorial symbols were highlighted concurrently. RESULTS The proposed display appears feasible, eliciting all targeted ERPs. However, ERP amplitudes may be reduced in comparison to single-item animation highlighting, possibly due to distraction. Ratings of mental effort were significantly higher for the 100 ms condition, though differences in the frontal P200/P300 ERP did not achieve significance. Most participants identified a preference for the 100 ms condition, though age may impact preference. CONCLUSIONS Overall, findings support the preliminary feasibility of a proposed 28-item interface that utilises group zoom animation highlighting of pictorial symbols. Further research is needed evaluating ERP characteristics and outcomes from online (real-time) use of animation-based P300-BCI-AAC for children with severe speech and physical impairments across multiple training sessions.
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Affiliation(s)
- Kevin M Pitt
- Department of Special Education and Communication Disorders, University of NE-Lincoln, Lincoln, NE, USA
| | - Austin Spoor
- Department of Special Education and Communication Disorders, University of NE-Lincoln, Lincoln, NE, USA
| | - Joshua Zosky
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
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Mobaien A, Boostani R, Sanei S. Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering. J Neural Eng 2024; 21:016023. [PMID: 38295418 DOI: 10.1088/1741-2552/ad2495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 01/31/2024] [Indexed: 02/02/2024]
Abstract
Objective.the P300-based brain-computer interface (BCI) establishes a communication channel between the mind and a computer by translating brain signals into commands. These systems typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience an elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. While various approaches have been explored to enhance the SNR of P300 signals, the impact of VEPs has been largely overlooked. The main objective of this study is to investigate how VEPs impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs.Approach.our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality.Main results.analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs demonstrates improved performance in P300-based BCIs. This improvement is verified through several experiments conducted with real P300 data.Significance.this study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.
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Affiliation(s)
- Ali Mobaien
- Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Reza Boostani
- Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, United Kingdom
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Lee J, Kim M, Heo D, Kim J, Kim MK, Lee T, Park J, Kim H, Hwang M, Kim L, Kim SP. A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning. Front Hum Neurosci 2024; 18:1320457. [PMID: 38361913 PMCID: PMC10867822 DOI: 10.3389/fnhum.2024.1320457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/08/2024] [Indexed: 02/17/2024] Open
Abstract
Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.
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Affiliation(s)
- Jongmin Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Minju Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Dojin Heo
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jongsu Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Min-Ki Kim
- The Institute of Healthcare Convergence, College of Medicine, Catholic Kwandong University, Gangneung-si, Republic of Korea
| | - Taejun Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Jongwoo Park
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - HyunYoung Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Minho Hwang
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Laehyun Kim
- Center for Bionics, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
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5
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Mijani A, Cherloo MN, Tang H, Zhan L. Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI. Comput Biol Med 2023; 166:107488. [PMID: 37778215 DOI: 10.1016/j.compbiomed.2023.107488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/26/2023] [Accepted: 09/15/2023] [Indexed: 10/03/2023]
Abstract
The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance. Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter. To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively. These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method's ability to generalize well in SSVEP analysis.
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Affiliation(s)
- AmirMohammad Mijani
- Department of Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, 15261, PA, USA.
| | - Mohammad Norizadeh Cherloo
- Department of Biomedical Engineering, University of Science and Technology (IUST), Narmak, Tehran, 16846-13114, Tehran, Iran.
| | - Haoteng Tang
- Department of Computer Science, University of Texas Rio Grande Valley, 1201 W University Dr, Edinburg, 78539, TX, USA.
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, 15261, PA, USA.
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Han J, Xu M, Xiao X, Yi W, Jung TP, Ming D. A high-speed hybrid brain-computer interface with more than 200 targets. J Neural Eng 2023; 20:016025. [PMID: 36608342 DOI: 10.1088/1741-2552/acb105] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/06/2023] [Indexed: 01/07/2023]
Abstract
Objective. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets.Approach. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments.Main results.The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37% ± 7.49% and 86.00% ± 5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83 ± 39.20 bits min-1and 204.47 ± 37.56 bits min-1, respectively. Notably, the peak ITR could reach up to 367.83 bits min-1.Significance.This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.
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Affiliation(s)
- Jin Han
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xiaolin Xiao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weibo Yi
- Beijing Machine and Equipment Institute, Beijing 100854, People's Republic of China
| | - Tzyy-Ping Jung
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Swartz Centre for Computational Neuroscience, University of California, San Diego, CA, United States of America
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
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Pan J, Chen X, Ban N, He J, Chen J, Huang H. Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation. Front Hum Neurosci 2022; 16:1077717. [PMID: 36618996 PMCID: PMC9810759 DOI: 10.3389/fnhum.2022.1077717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.
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Affiliation(s)
- Jiahui Pan
- *Correspondence: Jiahui Pan Haiyun Huang
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8
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Gannouni S, Belwafi K, Alangari N, AboAlsamh H, Belghith A. Classification Strategies for P300-Based BCI-Spellers Adopting the Row Column Paradigm. SENSORS (BASEL, SWITZERLAND) 2022; 22:9159. [PMID: 36501860 PMCID: PMC9738959 DOI: 10.3390/s22239159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Acknowledging the importance of the ability to communicate with other people, the researcher community has developed a series of BCI-spellers, with the goal of regaining communication and interaction capabilities with the environment for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, we believe that the development of efficient signal processing algorithms and strategies will go a long way towards achieving novel assistive technologies using new human-computer interfaces. In this paper, we present various classification strategies that would be adopted by P300 spellers adopting the row/column paradigm. The presented strategies have obtained high accuracy rates compared with existent similar research works.
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Affiliation(s)
- Sofien Gannouni
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Kais Belwafi
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
- Electrical Engineering and Computer Science Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Nourah Alangari
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Hatim AboAlsamh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Abdelfettah Belghith
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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9
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Brain-computer interface (BCI)-generated speech to control domotic devices. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Rathi N, Singla R, Tiwari S. A comparative study of classification methods for designing a pictorial P300-based authentication system. Med Biol Eng Comput 2022; 60:2899-2916. [DOI: 10.1007/s11517-022-02626-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/24/2022] [Indexed: 10/15/2022]
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11
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Chen XJ, Collins LM, Mainsah BO. Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication. CONFERENCE PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS 2022; 2022:1642-1647. [PMID: 36776946 PMCID: PMC9910722 DOI: 10.1109/smc53654.2022.9945561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Brain-computer interfaces (BCIs), such as the P300 speller, can provide a means of communication for individuals with severe neuromuscular limitations. BCIs interpret electroencephalography (EEG) signals in order to translate embedded information about a user's intent into executable commands to control external devices. However, EEG signals are inherently noisy and nonstationary, posing a challenge to extended BCI use. Conventionally, a BCI classifier is trained via supervised learning in an offline calibration session; once trained, the classifier is deployed for online use and is not updated. As the statistics of a user's EEG data change over time, the performance of a static classifier may decline with extended use. It is therefore desirable to automatically adapt the classifier to current data statistics without requiring offline recalibration. In an existing semi-supervised learning approach, the classifier is trained on labeled EEG data and is then updated using incoming unlabeled EEG data and classifier-predicted labels. To reduce the risk of learning from incorrect predictions, a threshold is imposed to exclude unlabeled data with low-confidence label predictions from the expanded training set when retraining the adaptive classifier. In this work, we propose the use of a language model for spelling error correction and disambiguation to provide information about label correctness during semi-supervised learning. Results from simulations with multi-session P300 speller user EEG data demonstrate that our language-guided semi-supervised approach significantly improves spelling accuracy relative to conventional BCI calibration and threshold-based semi-supervised learning.
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Affiliation(s)
- Xinlin J. Chen
- Duke University,Department of Electrical and Computer Engineering,Durham,NC,USA
| | - Leslie M. Collins
- Duke University,Department of Electrical and Computer Engineering,Durham,NC,USA
| | - Boyla O. Mainsah
- Duke University,Department of Electrical and Computer Engineering,Durham,NC,USA
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Peters B, Eddy B, Galvin-McLaughlin D, Betz G, Oken B, Fried-Oken M. A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities. Front Hum Neurosci 2022; 16:952380. [PMID: 35966988 PMCID: PMC9374067 DOI: 10.3389/fnhum.2022.952380] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Augmentative and alternative communication brain-computer interface (AAC-BCI) systems are intended to offer communication access to people with severe speech and physical impairment (SSPI) without requiring volitional movement. As the field moves toward clinical implementation of AAC-BCI systems, research involving participants with SSPI is essential. Research has demonstrated variability in AAC-BCI system performance across users, and mixed results for comparisons of performance for users with and without disabilities. The aims of this systematic review were to (1) describe study, system, and participant characteristics reported in BCI research, (2) summarize the communication task performance of participants with disabilities using AAC-BCI systems, and (3) explore any differences in performance for participants with and without disabilities. Electronic databases were searched in May, 2018, and March, 2021, identifying 6065 records, of which 73 met inclusion criteria. Non-experimental study designs were common and sample sizes were typically small, with approximately half of studies involving five or fewer participants with disabilities. There was considerable variability in participant characteristics, and in how those characteristics were reported. Over 60% of studies reported an average selection accuracy ≤70% for participants with disabilities in at least one tested condition. However, some studies excluded participants who did not reach a specific system performance criterion, and others did not state whether any participants were excluded based on performance. Twenty-nine studies included participants both with and without disabilities, but few reported statistical analyses comparing performance between the two groups. Results suggest that AAC-BCI systems show promise for supporting communication for people with SSPI, but they remain ineffective for some individuals. The lack of standards in reporting outcome measures makes it difficult to synthesize data across studies. Further research is needed to demonstrate efficacy of AAC-BCI systems for people who experience SSPI of varying etiologies and severity levels, and these individuals should be included in system design and testing. Consensus in terminology and consistent participant, protocol, and performance description will facilitate the exploration of user and system characteristics that positively or negatively affect AAC-BCI use, and support innovations that will make this technology more useful to a broader group of people. Clinical trial registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018095345, PROSPERO: CRD42018095345.
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Affiliation(s)
- Betts Peters
- Consortium for Accessible Multimodal Brain-Body Interfaces, United States
- REKNEW Projects, Institute on Development and Disability, Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
| | - Brandon Eddy
- Consortium for Accessible Multimodal Brain-Body Interfaces, United States
- REKNEW Projects, Institute on Development and Disability, Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
- Speech and Hearing Sciences Department, Portland State University, Portland, OR, United States
| | - Deirdre Galvin-McLaughlin
- Consortium for Accessible Multimodal Brain-Body Interfaces, United States
- REKNEW Projects, Institute on Development and Disability, Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
| | - Gail Betz
- Health Sciences and Human Services Library, University of Maryland, Baltimore, MD, United States
| | - Barry Oken
- Consortium for Accessible Multimodal Brain-Body Interfaces, United States
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Melanie Fried-Oken
- Consortium for Accessible Multimodal Brain-Body Interfaces, United States
- REKNEW Projects, Institute on Development and Disability, Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
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13
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Zhao X, Jin J, Xu R, Li S, Sun H, Wang X, Cichocki A. A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller. Front Hum Neurosci 2022; 16:875851. [PMID: 35754766 PMCID: PMC9231363 DOI: 10.3389/fnhum.2022.875851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
The P300-based brain-computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.
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Affiliation(s)
- Xueqing Zhao
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
- Shenzhen Research Institute of East China University of Technology, Shenzhen, China
| | - Ren Xu
- g.tec medical engineering GmbH, Graz, Austria
| | - Shurui Li
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Hao Sun
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology, Moscow, Russia
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
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14
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Pitt KM, Mansouri A, Wang Y, Zosky J. Toward P300-brain-computer interface access to contextual scene displays for AAC: An initial exploration of context and asymmetry processing in healthy adults. Neuropsychologia 2022; 173:108289. [PMID: 35690117 DOI: 10.1016/j.neuropsychologia.2022.108289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/04/2022] [Accepted: 06/04/2022] [Indexed: 11/16/2022]
Abstract
Brain-computer interfaces for augmentative and alternative communication (BCI-AAC) may help overcome physical barriers to AAC access. Traditionally, visually based P300-BCI-AAC displays utilize a symmetrical grid layout. Contextual scene displays are composed of context-rich images (e.g., photographs) and may support AAC success. However, contextual scene displays contrast starkly with the standard P300-grid approach. Understanding the neurological processes from which BCI-AAC devices function is crucial to human-centered computing for BCI-AAC. Therefore, the aim of this multidisciplinary investigation is to provide an initial exploration of contextual scene use for BCI-AAC. METHODS Participants completed three experimental conditions to evaluate the effects of item arrangement asymmetry and context on P300-based BCI-AAC signals and offline BCI-AAC accuracy, including 1) the full contextual scene condition, 2) asymmetrical item arraignment without context condition and 3) the grid condition. Following each condition, participants completed task-evaluation ratings (e.g., engagement). Offline BCI-AAC accuracy for each condition was evaluated using cross-validation. RESULTS Display asymmetry significantly decreased P300 latency in the centro-parietal cluster. P300 amplitudes in the frontal cluster were decreased, though nonsignificantly. Display context significantly increased N170 amplitudes in the occipital cluster, and N400 amplitudes in the centro-parietal and occipital clusters. Scenes were rated as more visually appealing and engaging, and offline BCI-AAC performance for the scene condition was not statistically different from the grid standard. CONCLUSION Findings support the feasibility of incorporating scene-based displays for P300-BCI-AAC development to help provide communication for individuals with minimal or emerging language and literacy skills.
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Affiliation(s)
- Kevin M Pitt
- Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Amirsalar Mansouri
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yingying Wang
- Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Joshua Zosky
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
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15
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Riccio A, Schettini F, Galiotta V, Giraldi E, Grasso MG, Cincotti F, Mattia D. Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis. Front Hum Neurosci 2022; 16:868419. [PMID: 35721361 PMCID: PMC9204311 DOI: 10.3389/fnhum.2022.868419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/26/2022] [Indexed: 11/25/2022] Open
Abstract
Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication “Grid 3.” The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (± 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers’ daily practice.
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Affiliation(s)
- Angela Riccio
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
- *Correspondence: Angela Riccio,
| | - Francesca Schettini
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Valentina Galiotta
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Enrico Giraldi
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | | | - Febo Cincotti
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
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16
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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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17
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Han J, Liu C, Chu J, Xiao X, Chen L, Xu M, Ming D. Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid Brain-computer interfaces. J Neurosci Methods 2022; 372:109535. [PMID: 35202615 DOI: 10.1016/j.jneumeth.2022.109535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/25/2022] [Accepted: 02/18/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Recently, we have implemented a high-speed brain-computer interface (BCI) system with a large instruction set using the concurrent P300 and steady-state visual evoked potential (SSVEP) features (also known as hybrid features). However, it remains unclear how to select inter-stimulus interval (ISI) for the proposed BCI system to balance the encoding efficiency and decoding performance. NEW METHOD This study developed a 6⁎9 hybrid P300-SSVEP BCI system and investigated a series of ISIs ranged from -175ms to 0ms with a step of 25ms. The influence of ISI on the hybrid features was analyzed from several aspects, including the amplitude of the induced features, classification accuracy, information transfer rate (ITR). Twelve naive subjects were recruited for the experiment. RESULTS The results showed the ISI factor had a significant impact on the hybrid features. Specifically, as the values of ISI decreased, the amplitudes of the induced features and accuracies decreased gradually, while the ITRs increased rapidly. It's achieved the highest ITR of 158.50 bits/min when ISI equal to -175ms. COMPARISON WITH EXISTING METHOD The optimal ISI in this study achieved superior performance in comparison with the one we used in the previous study. CONCLUSIONS The ISI can exert an important influence on the P300-SSVEP BCI system and its optimal value is -175ms in this study, which is significant for developing the high-speed BCI system with larger instruction sets in the future.
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Affiliation(s)
- Jin Han
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Chuan Liu
- Division of Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Jiayue Chu
- Division of Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xiaolin Xiao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.
| | - Long Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
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18
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Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103338] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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19
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20
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Palumbo A, Ielpo N, Calabrese B. An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals. SENSORS (BASEL, SWITZERLAND) 2022; 22:318. [PMID: 35009860 PMCID: PMC8749705 DOI: 10.3390/s22010318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/25/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.
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21
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Ma T, Li Y, Huggins JE, Zhu J, Kang J. Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface. J Am Stat Assoc 2022; 117:1122-1133. [PMID: 36313593 PMCID: PMC9609845 DOI: 10.1080/01621459.2022.2041422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.
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Affiliation(s)
- Tianwen Ma
- Department of Biostatistics, University of Michigan
| | - Yang Li
- Department of Statistics, University of Michigan
| | - Jane E Huggins
- Department of Physical Medicine and Rehabilitation and Department of Biomedical Engineering, University of Michigan
| | - Ji Zhu
- Department of Statistics, University of Michigan
| | - Jian Kang
- Department of Biostatistics, University of Michigan
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22
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Loizidou P, Rios E, Marttini A, Keluo-Udeke O, Soetedjo J, Belay J, Perifanos K, Pouratian N, Speier W. Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller. BRAIN-COMPUTER INTERFACES 2022; 9:36-48. [PMID: 35574291 PMCID: PMC9094140 DOI: 10.1080/2326263x.2021.1993426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world.
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Affiliation(s)
- P Loizidou
- Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - E Rios
- Radiology, Stanford University, Stanford, CA 94305, USA
| | - A Marttini
- Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - O Keluo-Udeke
- Computer Science, University of Arkansas at Pine Bluff, Pine Bluff, AR 71601, USA
| | - J Soetedjo
- Bioengineering, University of Washington, Seattle, Washington 98195, USA
| | - J Belay
- Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - K Perifanos
- Linguistics, National and Kapodistrian University of Athens, Athens, Attica 15784, Greece
| | - N Pouratian
- Neurosurgery, University of California, Los Angeles, Los Angeles CA 90024, USA
| | - W Speier
- Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90024, USA,Corresponding Author: 924 Westwood Blvd, Suite 600, Los Angeles, CA 90024, (215) 206-6007,
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23
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Ma T, Huggins JE, Kang J. Adaptive Sequence-Based Stimulus Selection in an ERP-Based Brain-Computer Interface by Thompson Sampling in a Multi-Armed Bandit Problem. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:3648-3655. [PMID: 35692622 DOI: 10.1109/bibm52615.2021.9669724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A Brain-Computer Interface (BCI) is a device that interprets brain activity to help people with disabilities communicate. The P300 ERP-based BCI speller displays a series of events on the screen and searches the elicited electroencephalogram (EEG) data for target P300 event-related potential (ERP) responses among a series of non-target events. The Checkerboard (CB) paradigm is a common stimulus presentation paradigm. Although a few studies have proposed data-driven methods for stimulus selection, they suffer from intractable decision rules, large computation complexity, or error propagation for participants who perform poorly under the static paradigm. In addition, none of the methods have been applied to the CB paradigm directly. In this work, we propose a sequence-based adaptive stimulus selection method using Thompson Sampling in the multi-bandit problem with multiple actions. During each sequence, the algorithm selects a random subset of stimuli with fixed size, aiming to identify all target stimuli and to improve the spelling speed by reducing the number of unnecessary non-target stimuli. We compute "clean" stimulus-specific rewards from raw classifier scores via the Bayes rule. We perform extensive simulation studies to compare our algorithm to the static CB paradigm. We show the robustness of our algorithm by considering the constraints of practical use. For scenarios where simulated data resemble the real data the most, the spelling efficiency of our algorithm increases by more than 70%, compared to the static CB paradigm.
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Affiliation(s)
- Tianwen Ma
- Dept. of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Jane E Huggins
- Dept. of Physical Med. & Rehabilitation, University of Michigan, Ann Arbor, USA
| | - Jian Kang
- Dept. of Biostatistics, University of Michigan, Ann Arbor, USA
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24
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Gao P, Huang Y, He F, Qi H. Improve P300-speller performance by online tuning stimulus onset asynchrony (SOA). J Neural Eng 2021; 18. [PMID: 34638106 DOI: 10.1088/1741-2552/ac2f04] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 10/12/2021] [Indexed: 11/12/2022]
Abstract
Objective. The P300-Speller is a classic brain-computer interface paradigm that has been subjected to numerous clinical trials. Some studies have reported that the performance of the P300-Speller is closely related to stimulus onset asynchrony (SOA), but very few studies have attempted to improve the performance of the P300-Speller by optimizing SOA.Approach.In this paper, we designed a P300-Speller system based on a variable SOA and dynamic stop strategy, which can automatically adjust SOA according to real-time operational performance.Main results.The online experiment results of 18 subjects showed that the event-related potential classifier and the dynamic stop algorithm established at 200 ms SOA can maintain the performance at a certain level among 50-300 ms SOA. The system can then reduce the SOA from an initial 200 ms to an average of about 98.5 ms while maintaining letter output accuracy. The average theoretical information transfer rate was significantly improved from 42.4 to 85 bit min-1(the maximum was 232 bit min-1).Significance.These results demonstrate that the system established in this paper can automatically optimize the SOA settings, and this personalized SOA adjustment can effectively improve the performance of the P300-Speller.
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Affiliation(s)
- Pin Gao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yihao Huang
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Feng He
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Hongzhi Qi
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
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Chen XJ, Collins LM, Mainsah BO. Mitigating the Impact of Psychophysical Effects During Adaptive Stimulus Selection in the P300 Speller Brain-Computer Interface. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5796-5799. [PMID: 34892437 PMCID: PMC8762976 DOI: 10.1109/embc46164.2021.9630048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using sensory stimuli to elicit specific neural signal components called event-related potentials (ERPs) to control external devices. However, psychophysical factors, such as refractory effects and adjacency distractions, may negatively impact ERP elicitation and BCI performance. Although conventional BCI stimulus presentation paradigms usually design stimulus presentation schedules in a pseudo-random manner, recent studies have shown that controlling the stimulus selection process can enhance ERP elicitation. In prior work, we developed an algorithm to adaptively select BCI stimuli using an objective criterion that maximizes the amount of information about the user's intent that can be elicited with the presented stimuli given current data conditions. Here, we enhance this adaptive BCI stimulus selection algorithm to mitigate adjacency distractions and refractory effects by modeling temporal dependencies of ERP elicitation in the objective function and imposing spatial restrictions in the stimulus search space. Results from simulations using synthetic data and human data from a BCI study show that the enhanced adaptive stimulus selection algorithm can improve spelling speeds relative to conventional BCI stimulus presentation paradigms.Clinical relevance-Increased communication rates with our enhanced adaptive stimulus selection algorithm can potentially facilitate the translation of BCIs as viable communication alternatives for individuals with severe neuromuscular limitations.
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26
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Zhang X, Jin J, Li S, Wang X, Cichocki A. Evaluation of color modulation in visual P300-speller using new stimulus patterns. Cogn Neurodyn 2021; 15:873-886. [PMID: 34603548 DOI: 10.1007/s11571-021-09669-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/04/2021] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
Objective The stimulus color of P300-BCI systems has been successfully modified. However, the effects of different color combinations have not been widely investigated. In this study, we designed new stimulus patterns to evaluate the influence of color modulation on the BCI performance and waveforms of the evoked related potential (ERP).Methods Comparison was performed for three new stimulus patterns consisting of red face and colored block-shape, namely, red face with a white rectangle (RFW), red face with a blue rectangle (RFB), and red face with a red rectangle (RFR). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. Repeated-measures ANOVA and Bonferroni correction were applied for statistical analysis. Results The RFW pattern obtained the highest average online accuracy with 96.94%, and those of RFR and RFB patterns were 93.61% and of 92.22% respectively. Significant differences in online accuracy and information transfer rate (ITR) were found between RFW and RFR patterns (p < 0.05). Conclusion Compared with RFR and RFB patterns, RFW yielded the best performance in P300-BCI. These new stimulus patterns with different color combinations have considerable importance to BCI applications and user-friendliness.
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Affiliation(s)
- Xinru Zhang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Shurui Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xingyu Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia.,Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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27
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Adebayo OG, Onasanwo SA, Ajayi AM, Aduema W, Oyebanjo OT, Nicodemus OU. Cnidoscolus aconitifolius-supplemented diet enhanced neurocognition, endogenous antioxidants and cholinergic system and maintains hippocampal neuronal integrity in male Wistar rats. Drug Metab Pers Ther 2021; 37:81-93. [PMID: 35385891 DOI: 10.1515/dmpt-2021-0138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/07/2021] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Cnidoscolus aconitifolius have been investigated to have abundant phytochemicals. However, study on the effect of Cnidoscolus aconitifolius on neurobehavioral performance when supplemented with diet is lacking. The study is aimed at investigating the memory-enhancing effect of Cnidoscolus aconitifolius-supplemented diet (CAD) using Morris water maze and Novel object recognition test. METHODS Ninety male Wistar rats (80-100 g) were fed with CAD (1, 2.5, 5 and 10%) continuously for a period of 4, 8 and 12 weeks respectively. Six animals per group were used for assessment of memory performance (Morris water maze [MWM] and Novel object recognition test [NORT]); afterwards the brain tissues were harvested for malondialdehyde (MDA), glutathione (GSH) and catalase (CAT) estimation. Acetylcholinesterase (AChE) concentration was also determined. Hippocampal architectural change in the neuron was examined using hematoxylin and eosin (H&E) and cresyl fast violet (Nissl) stain. RESULTS Higher percentage of CAD significantly (p<0.05) improve memory performance with time-dependent effects in rats fed with CAD on MMW and NORT. MDA significantly (p<0.05) reduce in 1 and 2.5% CAD groups at 4th weeks and in 2.5 and 5% CAD groups at 8th weeks while GSH concentration significantly (p<0.05) increase at 12th weeks in 2.5 and 10% CAD groups. However, CAT concentration significantly (p<0.05) increase in 2.5, and 5%, CAD groups, 1, 5, and 10% CAD groups and in 5, and 10% CAD groups at 4th, 8th and 12th weeks. AChE significantly (p<0.05) reduce at 4th and 12th weeks. Histological assessment reveals no neuronal and pyramidal degeneration (chromatolysis) at the hippocampal Cornu Ammonis 3 (CA3) region. CONCLUSIONS The results suggest that CAD boost memory performance in rats through positive modulation of oxidative stress, cholinergic system and degeneration of hippocampal neurons.
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Affiliation(s)
- Olusegun G Adebayo
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria.,Department of Physiology, Neurophysiology Unit, PAMO University of Medical Sciences, Port-Harcourt, Nigeria
| | - Samuel A Onasanwo
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria
| | - Abayomi M Ajayi
- Department of Pharmacology, Neuropharmacology Unit, University of Ibadan, Ibadan, Nigeria
| | - Wadioni Aduema
- Department of Physiology, Faculty of Basic Medical Sciences, Bayelsa Medical University, Yenagoa, Bayelsa State, Nigeria
| | - Oyetola T Oyebanjo
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria.,Department of Physiology, Babcock University, Ilishan-Remo, Nigeria
| | - Omeje U Nicodemus
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria
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Adebayo OG, Onasanwo SA, Ajayi AM, Aduema W, Oyebanjo OT, Nicodemus OU. Cnidoscolus aconitifolius-supplemented diet enhanced neurocognition, endogenous antioxidants and cholinergic system and maintains hippocampal neuronal integrity in male Wistar rats. Drug Metab Pers Ther 2021; 0:dmdi-2021-0138. [PMID: 34390637 DOI: 10.1515/dmdi-2021-0138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/07/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Cnidoscolus aconitifolius have been investigated to have abundant phytochemicals. However, study on the effect of Cnidoscolus aconitifolius on neurobehavioral performance when supplemented with diet is lacking. The study is aimed at investigating the memory-enhancing effect of Cnidoscolus aconitifolius-supplemented diet (CAD) using Morris water maze and Novel object recognition test. METHODS Ninety male Wistar rats (80-100 g) were fed with CAD (1, 2.5, 5 and 10%) continuously for a period of 4, 8 and 12 weeks respectively. Six animals per group were used for assessment of memory performance (Morris water maze [MWM] and Novel object recognition test [NORT]); afterwards the brain tissues were harvested for malondialdehyde (MDA), glutathione (GSH) and catalase (CAT) estimation. Acetylcholinesterase (AChE) concentration was also determined. Hippocampal architectural change in the neuron was examined using hematoxylin and eosin (H&E) and cresyl fast violet (Nissl) stain. RESULTS Higher percentage of CAD significantly (p<0.05) improve memory performance with time-dependent effects in rats fed with CAD on MMW and NORT. MDA significantly (p<0.05) reduce in 1 and 2.5% CAD groups at 4th weeks and in 2.5 and 5% CAD groups at 8th weeks while GSH concentration significantly (p<0.05) increase at 12th weeks in 2.5 and 10% CAD groups. However, CAT concentration significantly (p<0.05) increase in 2.5, and 5%, CAD groups, 1, 5, and 10% CAD groups and in 5, and 10% CAD groups at 4th, 8th and 12th weeks. AChE significantly (p<0.05) reduce at 4th and 12th weeks. Histological assessment reveals no neuronal and pyramidal degeneration (chromatolysis) at the hippocampal Cornu Ammonis 3 (CA3) region. CONCLUSIONS The results suggest that CAD boost memory performance in rats through positive modulation of oxidative stress, cholinergic system and degeneration of hippocampal neurons.
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Affiliation(s)
- Olusegun G Adebayo
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria
- Department of Physiology, Neurophysiology Unit, PAMO University of Medical Sciences, Port-Harcourt, Nigeria
| | - Samuel A Onasanwo
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria
| | - Abayomi M Ajayi
- Department of Pharmacology, Neuropharmacology Unit, University of Ibadan, Ibadan, Nigeria
| | - Wadioni Aduema
- Department of Physiology, Faculty of Basic Medical Sciences, Bayelsa Medical University, Yenagoa, Bayelsa State, Nigeria
| | - Oyetola T Oyebanjo
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria
- Department of Physiology, Babcock University, Ilishan-Remo, Nigeria
| | - Omeje U Nicodemus
- Department of Physiology, Neurosciences and Oral Physiology Unit, University of Ibadan, Ibadan, Nigeria
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Xu L, Xu M, Jung TP, Ming D. Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface. Cogn Neurodyn 2021; 15:569-584. [PMID: 34367361 PMCID: PMC8286913 DOI: 10.1007/s11571-021-09676-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/10/2021] [Accepted: 03/26/2021] [Indexed: 01/04/2023] Open
Abstract
A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.
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Affiliation(s)
- Lichao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Tzyy-Ping Jung
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Swartz Center for Computational Neuroscience, University of California, San Diego, USA
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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Yang C, Yan X, Wang Y, Chen Y, Zhang H, Gao X. Spatio-temporal equalization multi-window algorithm for asynchronous SSVEP-based BCI. J Neural Eng 2021; 18. [PMID: 34237711 DOI: 10.1088/1741-2552/ac127f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/08/2021] [Indexed: 11/11/2022]
Abstract
Objective.Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data.Approach.The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the 'non-control state detection' methods, this algorithm was based on the 'statistical inspection-rejection decision' mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates.Main results.Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of97.2±2.6%and the average information transfer rate (ITR) of106.3±32.0 bitsmin-1. At the same time, the average false alarm rate in the 240 s resting state test was0.607±0.602 min-1. In the free spelling experiments involving patients with severe amyotrophic lateral sclerosis, the subjects achieved an accuracy of 92.7% and an average ITR of 43.65 bits min-1in two free spelling experiments.Significance.This algorithm can achieve high-performance, high-precision, and asynchronous detection of SSVEP signals with low algorithm complexity and false alarm rate under multi-targets and training-free conditions, which is helpful for the development of asynchronous BCI systems.
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Affiliation(s)
- Chen Yang
- School of Electronic Engineering, Beijing University of Posts and Telecommunications.,School of Medicine, Tsinghua University
| | - Xinyi Yan
- School of Medicine, Tsinghua University
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences
| | | | - Hongxin Zhang
- School of Electronic Engineering, Beijing University of Posts and Telecommunications
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Velasco-Álvarez F, Fernández-Rodríguez Á, Vizcaíno-Martín FJ, Díaz-Estrella A, Ron-Angevin R. Brain-Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications. SENSORS (BASEL, SWITZERLAND) 2021; 21:3716. [PMID: 34073602 PMCID: PMC8199460 DOI: 10.3390/s21113716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 12/13/2022]
Abstract
Brain-computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.
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Affiliation(s)
- Francisco Velasco-Álvarez
- Departamento de Tecnología Electrónica, Universidad de Málaga, 29071 Málaga, Spain; (Á.F.-R.); (F.-J.V.-M.); (A.D.-E.); (R.R.-A.)
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32
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High-performance brain-to-text communication via handwriting. Nature 2021; 593:249-254. [PMID: 33981047 DOI: 10.1038/s41586-021-03506-2] [Citation(s) in RCA: 248] [Impact Index Per Article: 82.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 03/26/2021] [Indexed: 12/14/2022]
Abstract
Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping1-5 or point-and-click typing with a computer cursor6,7. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)8. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.
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Yu X, da Silva-Sauer L, Donchin E. Habituation of P300 in the Use of P300-based Brain-Computer Interface Spellers: Individuals With Amyotrophic Lateral Sclerosis Versus Age-Matched Controls. Clin EEG Neurosci 2021; 52:221-230. [PMID: 32419492 DOI: 10.1177/1550059420918755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The P300-based brain-computer interface speller can provide motor independent communication to individuals with amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disorder that affects the motor system. P300 amplitude stability is critical for operation of the P300 speller. The P300 has good long-term stability, but to our knowledge, short-term habituation in the P300 speller has not been studied. In the current study, 15 participants: 8 ALS patients and 7 age-matched healthy volunteers (HVs), used 2 versions of P300 spellers, Face speller and Flash speller, each for 30 minutes. The ALS group performed as well as the HVs in both spellers and HVs did better with the Face speller than Flash speller while the ALS group performed equally well in both spellers. Neither intra-run P300 habituation nor inter-run P300 habituation was found. The P300 speller could be a reliable communication device for individuals with ALS.
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Affiliation(s)
- Xiaoqian Yu
- Department of Psychology, 7831University of South Florida, Tampa, FL, USA
| | - Leandro da Silva-Sauer
- Department of Psychology, 7831University of South Florida, Tampa, FL, USA.,123204Federal University of Paraiba, João Pessoa, Paraiba, Brazil
| | - Emanuel Donchin
- Department of Psychology, 7831University of South Florida, Tampa, FL, USA
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Ma R, Yu T, Zhong X, Yu ZL, Li Y, Gu Z. Capsule Network for ERP Detection in Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2021; 29:718-730. [PMID: 33793402 DOI: 10.1109/tnsre.2021.3070327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.
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35
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Li M, He D, Li C, Qi S. Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance. Brain Sci 2021; 11:450. [PMID: 33916189 PMCID: PMC8065759 DOI: 10.3390/brainsci11040450] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/17/2022] Open
Abstract
The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain-computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.
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Affiliation(s)
- Minglun Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
- Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110169, China
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36
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Laport F, Iglesia D, Dapena A, Castro PM, Vazquez-Araujo FJ. Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces. SENSORS (BASEL, SWITZERLAND) 2021; 21:2220. [PMID: 33810122 PMCID: PMC8004835 DOI: 10.3390/s21062220] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/03/2022]
Abstract
Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user's intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms-one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.
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Affiliation(s)
- Francisco Laport
- CITIC Research Center, University of A Coruña, Campus de Elviña, 15071 A Coruña, Spain; (D.I.); (A.D.); (P.M.C.); (F.J.V.-A.)
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37
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Fernández-Rodríguez Á, Medina-Juliá MT, Velasco-Álvarez F, Ron-Angevin R. Different effects of using pictures as stimuli in a P300 brain-computer interface under rapid serial visual presentation or row-column paradigm. Med Biol Eng Comput 2021; 59:869-881. [PMID: 33742353 DOI: 10.1007/s11517-021-02340-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 02/23/2021] [Indexed: 02/08/2023]
Abstract
Previous proposals for controlling a P300-based BCI speller have shown an improvement using alternative images instead of letters as target stimuli under a row-column paradigm (RCP). However, the RCP is not suitable for those patients with a lack of gaze control. To solve that, the rapid serial visual presentation (RSVP) paradigm has been proposed in previous studies. The aim of the present work is to assess if a set of alternative pictures that improved performance in RCP could also improve performance in RSVP. Sixteen participants controlled four conditions in calibration and online tasks: letters in RCP, pictures in RCP, letters in RSVP and pictures in RSVP. The effect given by pictures was greater under RCP than under RSVP, both for performance and event-related potential analyses. Indeed, pictures did not show any improvement under RSVP in comparison to letters. In addition, the condition with pictures under RCP was declared the favourite by most users (68.75%), while the condition with pictures under RSVP was not chosen as favourite by any participant. Therefore, this work shows that the improvement related to the use of pictures as alternative flashing stimuli under RCP may not be transferred to RSVP. Graphical abstract.
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Affiliation(s)
- Álvaro Fernández-Rodríguez
- Departamento de Tecnología Electrónica, Universidad de Málaga, 35 Louis Pasteur Boulevard, 29071, Malaga, Spain.
| | - María Teresa Medina-Juliá
- Departamento de Tecnología Electrónica, Universidad de Málaga, 35 Louis Pasteur Boulevard, 29071, Malaga, Spain
| | - Francisco Velasco-Álvarez
- Departamento de Tecnología Electrónica, Universidad de Málaga, 35 Louis Pasteur Boulevard, 29071, Malaga, Spain
| | - Ricardo Ron-Angevin
- Departamento de Tecnología Electrónica, Universidad de Málaga, 35 Louis Pasteur Boulevard, 29071, Malaga, Spain
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Abstract
Brain-computer interface (BCI) system aims to enable interaction with people and therefore the environment without muscular activation, using changes in brain signals due to the execution of cognitive tasks. The target of the presented work is to investigate the power of Emotiv EPOC + headset to detect and record the P300 wave. Moreover, the effect of preprocessing the acquired signal was studied. Five participants were asked to attend different sessions to an equivalent 6x6 matrix while the rows and columns were randomly flashed at a rate of 200 ms. The acquired EEG data were sent wirelessly to OpenViBE software, which is employed to run the P300 speller. Two classification methods were tried: Linear discriminate analysis (LDA) and support vector machine (SVM). The capability of the headset to detect the P300 signals is proven by the results. Additionally, results show that participants reached accuracy up to 90 and 70% after only two training sessions for Linear discriminate analysis (LDA) and support vector machine (SVM) classifiers, respectively. The significance of this work is to demonstrate that such a portable and affordable headset might be useful to design and implement a robust and reliable online P300-based BCI system.
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Affiliation(s)
- Islam A Fouad
- Biomedical Engineering Department, MUST University, Giza, Egypt
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39
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Mao Y, Jin J, Xu R, Li S, Miao Y, Cichocki A. The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm. Int J Neural Syst 2021; 31:2150004. [PMID: 33438531 DOI: 10.1142/s0129065721500040] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached the stimulators on the subject's cheeks. Two paradigms were designed as follows: a paradigm with no visual attention and another paradigm with visual attention to the target position. Eleven subjects were invited to perform the two paradigms. We also recorded and analyzed the eyeball movement data during the paradigm with visual attention to explore whether the eyeball movement would have an effect on the BCI classification. The average online accuracy was 89.09% for the paradigm with visual attention, which was significantly higher than that of the paradigm with no visual attention (70.45%). Significant difference in ITR was also found between the two paradigms ([Formula: see text]). The results demonstrated that visual attention was an effective method to improve the performance of tactile P300 BCI. Our findings suggested that it may be feasible to complete an efficient tactile BCI system by adding visual attention.
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Affiliation(s)
- Ying Mao
- Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China
| | - Jing Jin
- Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China
| | - Ren Xu
- Guger Technologies OG, Graz, Austria
| | - Shurui Li
- Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China
| | - Yangyang Miao
- Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China
| | - Andrzej Cichocki
- Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia.,Department of Applied Computer Science, Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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Chen X, Tao X, Wang FL, Xie H. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05588-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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41
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Shukla PK, Chaurasiya RK, Verma S. Performance improvement of P300-based home appliances control classification using convolution neural network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102220] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zisk AH, Borgheai SB, McLinden J, Hosni SM, Deligani RJ, Shahriari Y. P300 latency jitter and its correlates in people with amyotrophic lateral sclerosis. Clin Neurophysiol 2020; 132:632-642. [PMID: 33279436 DOI: 10.1016/j.clinph.2020.10.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 08/19/2020] [Accepted: 10/11/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE People with amyotrophic lateral sclerosis (ALS) can benefit from brain-computer interfaces (BCIs). However, users with ALS may experience significant variations in BCI performance and event-related potential (ERP) characteristics. This study investigated latency jitter and its correlates in ALS. METHODS Electroencephalographic (EEG) responses were recorded from six people with ALS and nine neurotypical controls. ERP amplitudes and latencies were extracted. Classifier-based latency estimation was used to calculate latency jitter. ERP components and latency jitter were compared between groups using Wilcoxon rank-sum tests. Correlations between latency jitter and each of the clinical measures, ERP features, and performance measures were investigated using Spearman and repeated measures correlations. RESULTS Latency jitter was significantly increased in participants with ALS and significantly negatively correlated with BCI performance in both ALS and control participants. ERP amplitudes were significantly attenuated in ALS, and significant correlations between ERP features and latency jitter were observed. There was no significant correlation between latency jitter and clinical measures. CONCLUSIONS Latency jitter is increased in ALS and correlates with both BCI performance and ERP features. SIGNIFICANCE These results highlight the associations of latency jitter with BCI performance and ERP characteristics and could inform future BCI designs for people with ALS.
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Affiliation(s)
- Alyssa Hillary Zisk
- University of Rhode Island, Interdisciplinary Neuroscience Program, Kingston, RI, USA.
| | - Seyyed Bahram Borgheai
- University of Rhode Island, Electrical, Computer, and Biomedical Engineering Department, Kingston, RI, USA
| | - John McLinden
- University of Rhode Island, Electrical, Computer, and Biomedical Engineering Department, Kingston, RI, USA
| | - Sarah M Hosni
- University of Rhode Island, Electrical, Computer, and Biomedical Engineering Department, Kingston, RI, USA
| | - Roohollah Jafari Deligani
- University of Rhode Island, Electrical, Computer, and Biomedical Engineering Department, Kingston, RI, USA
| | - Yalda Shahriari
- University of Rhode Island, Interdisciplinary Neuroscience Program, Kingston, RI, USA; University of Rhode Island, Electrical, Computer, and Biomedical Engineering Department, Kingston, RI, USA.
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Kirasirova L, Bulanov V, Ossadtchi A, Kolsanov A, Pyatin V, Lebedev M. A P300 Brain-Computer Interface With a Reduced Visual Field. Front Neurosci 2020; 14:604629. [PMID: 33343290 PMCID: PMC7744588 DOI: 10.3389/fnins.2020.604629] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/09/2020] [Indexed: 11/13/2022] Open
Abstract
A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.
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Affiliation(s)
| | - Vladimir Bulanov
- Laboratory of Mathematical Processing of Biological Information, IT Universe Ltd, Samara, Russia
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | | | | | - Mikhail Lebedev
- Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Department of Information and Internet Technologies of Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Center For Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, Russia
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44
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Geronimo A, Simmons Z. TeleBCI: remote user training, monitoring, and communication with an evoked-potential brain-computer interface. BRAIN-COMPUTER INTERFACES 2020; 7:57-69. [PMID: 33763499 DOI: 10.1080/2326263x.2020.1848134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Brain-computer interfaces (BCIs) are a movement-independent form of augmentative and alternative communication (AAC) for individuals with amyotrophic lateral sclerosis (ALS). The rare utilization of such devices in the homes of patients stems from a number of factors, one of which is the complexity of providing training and support for users. This paper describes the teleBCI interface used to train the patient and facilitator in the operation of a virtual keyboard using an evoked potential BCI. Fifteen patients with motor neuron disease and their communication partners were included in the study, participating from their homes while receiving remote support from the research team. Patient/caregiver teams completed 8 sessions each of P300 BCI training virtually with the researcher. As they participated in subsequent training sessions, participant teams required less help to complete physical, computer, and BCI-specific tasks associated with device use. A subset of users experienced improved performance over sessions, progressing to utilize the full functionality of the speller and communicate with a nurse partner over a telemedicine interface. Perceptions of device utility varied with accuracy of the BCI system. In the management of ALS, the integration of telemedicine provides new opportunities for care delivery, including how BCI-AAC are deployed and used.
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Affiliation(s)
- A Geronimo
- Department of Neurosurgery, 500 University Drive, Hershey, PA 17033
| | - Zachary Simmons
- Departments of Neurology and Humanities, 500 University Drive, Hershey, PA 17033
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45
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Xu M, Han J, Wang Y, Jung TP, Ming D. Implementing Over 100 Command Codes for a High-Speed Hybrid Brain-Computer Interface Using Concurrent P300 and SSVEP Features. IEEE Trans Biomed Eng 2020; 67:3073-3082. [DOI: 10.1109/tbme.2020.2975614] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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46
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Kimura M, Nakatani S, Nishida SI, Taketoshi D, Araki N. 3D Printable Dry EEG Electrodes with Coiled-Spring Prongs. SENSORS 2020; 20:s20174733. [PMID: 32825762 PMCID: PMC7506718 DOI: 10.3390/s20174733] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/18/2020] [Accepted: 08/18/2020] [Indexed: 11/20/2022]
Abstract
Various dry electroencephalography (EEG) electrodes have been developed. Dry EEG electrodes need to be pressed onto the scalp; therefore, there is a tradeoff between keeping the contact impedance low and maintaining comfort. We propose an approach to solve this tradeoff through the printing of complex-shaped electrodes by using a stereolithography 3D printer. To show the feasibility of our approach, we fabricated electrodes that have flexible fingers (prongs) with springs. Although dry electrodes with flexible prongs have been proposed, a suitable spring constant has not been obtained. In this study, the spring constant of our electrodes was determined from a contact model between the electrodes and the scalp. The mechanical properties and reproductivity of the electrodes were found to be sufficient. Finally, we measured the alpha waves when a participant opened/closed his eyes by using our electrodes.
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Affiliation(s)
- Masaya Kimura
- Graduate School of Sustainability Science, Tottori University, 4-101, Koyama-cho Minami, Tottori 680-8552, Japan; (M.K.); (S.-I.N.)
| | - Shintaro Nakatani
- Graduate School of Sustainability Science, Tottori University, 4-101, Koyama-cho Minami, Tottori 680-8552, Japan; (M.K.); (S.-I.N.)
- Advanced Mechanical and Electronic System Research Center, Faculty of Engineering, Tottori University, 4-101, Koyama-cho Minami, Tottori 680-8552, Japan
- Correspondence:
| | - Shin-Ichiro Nishida
- Graduate School of Sustainability Science, Tottori University, 4-101, Koyama-cho Minami, Tottori 680-8552, Japan; (M.K.); (S.-I.N.)
- Advanced Mechanical and Electronic System Research Center, Faculty of Engineering, Tottori University, 4-101, Koyama-cho Minami, Tottori 680-8552, Japan
| | - Daiju Taketoshi
- Technical Department, Tottori University, 4-101, Koyama-cho Minami, Tottori 680-8552, Japan;
| | - Nozomu Araki
- Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan;
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47
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Liu S, Wang W, Sheng Y, Zhang L, Xu M, Ming D. Improving the Cross-Subject Performance of the ERP-Based Brain-Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank. Front Hum Neurosci 2020; 14:296. [PMID: 32848671 PMCID: PMC7412932 DOI: 10.3389/fnhum.2020.00296] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 07/03/2020] [Indexed: 11/30/2022] Open
Abstract
The brain–computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for cross-subject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value of 0.8, while the traditional random selection only achieved 0.65. These encouraging results suggest that with proper evoked paradigm and classification methods, it is feasible to get stable performance across subjects for ERP-based BCI. Thus, our findings provide a new approach to improve BCI performances.
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Affiliation(s)
- Shuang Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Wei Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yue Sheng
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Ludan Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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48
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An WW, Pei A, Noyce AL, Shinn-Cunningham B. Decoding auditory attention from single-trial EEG for a high-efficiency brain-computer interface. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3456-3459. [PMID: 33018747 DOI: 10.1109/embc44109.2020.9175753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Brain-computer interface (BCI) systems enable humans to communicate with a machine in a non-verbal and covert way. Many past BCI designs used visual stimuli, due to the robustness of neural signatures evoked by visual input. However, these BCI systems can only be used when visual attention is available. This study proposes a new BCI design using auditory stimuli, decoding spatial attention from electroencephalography (EEG). Results show that this new approach can decode attention with a high accuracy (>75%) and has a high information transfer rate (>10 bits/min) compared to other auditory BCI systems. It also has the potential to allow decoding that does not depend on subject-specific training.
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49
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Rashid M, Sulaiman N, P P Abdul Majeed A, Musa RM, Ab Nasir AF, Bari BS, Khatun S. Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review. Front Neurorobot 2020; 14:25. [PMID: 32581758 PMCID: PMC7283463 DOI: 10.3389/fnbot.2020.00025] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 04/08/2020] [Indexed: 12/12/2022] Open
Abstract
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
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Affiliation(s)
- Mamunur Rashid
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Norizam Sulaiman
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Anwar P P Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Malaysia
| | - Ahmad Fakhri Ab Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Bifta Sama Bari
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Sabira Khatun
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
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50
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Fried-Oken M, Kinsella M, Peters B, Eddy B, Wojciechowski B. Human visual skills for brain-computer interface use: a tutorial. Disabil Rehabil Assist Technol 2020; 15:799-809. [PMID: 32476516 DOI: 10.1080/17483107.2020.1754929] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background and objectives: Many brain-computer interfaces (BCIs) for people with severe disabilities present stimuli in the visual modality with little consideration of the visual skills required for successful use. The primary objective of this tutorial is to present researchers and clinical professionals with basic information about the visual skills needed for functional use of visual BCIs, and to offer modifications that would render BCI technology more accessible for persons with vision impairments.Methods: First, we provide a background on BCIs that rely on a visual interface. We then describe the visual skills required for BCI technologies that are used for augmentative and alternative communication (AAC), as well as common eye conditions or impairments that can impact the user's performance. We summarize screening tools that can be administered by the non-eye care professional in a research or clinical setting, as well as the role of the eye care professional. Finally, we explore potential BCI design modifications to compensate for identified functional impairments. Information was generated from literature review and the clinical experience of vision experts.Results and conclusions: This in-depth description culminates in foundational information about visual skills and functional visual impairments that affect the design and use of visual interfaces for BCI technologies. The visual interface is a critical component of successful BCI systems. We can determine a BCI system for potential users with visual impairments and design BCI visual interfaces based on sound anatomical and physiological visual clinical science.Implications for RehabilitationAs brain-computer interfaces (BCIs) become possible access methods for people with severe motor impairments, it is critical that clinicians have a basic knowledge of the visual skills necessary for use of visual BCI interfaces.Rehabilitation providers must have a knowledge of objectively gathering information regarding a potential BCI user's functional visual skills.Rehabilitation providers must understand how to modify BCI visual interfaces for the potential user with visual impairments.Rehabilitation scientists should understand the visual demands of BCIs as they develop and evaluate these new access methods.
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Affiliation(s)
- Melanie Fried-Oken
- Departments of Neurology, Pediatrics, Biomedical Engineering, and Otolaryngology, Oregon Health & Science University, Portland, OR, USA.,Institute on Development and Disability, Oregon Health & Science University, Portland, OR, USA
| | - Michelle Kinsella
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR, USA
| | - Betts Peters
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR, USA
| | - Brandon Eddy
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR, USA.,Department of Speech and Hearing Sciences, Portland State University, Portland, OR, USA
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