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Ribeiro TF, Carriello MA, de Paula EP, Garcia AC, da Rocha GL, Teive HAG. Clinical applications of neurofeedback based on sensorimotor rhythm: a systematic review and meta-analysis. Front Neurosci 2023; 17:1195066. [PMID: 38053609 PMCID: PMC10694284 DOI: 10.3389/fnins.2023.1195066] [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: 03/28/2023] [Accepted: 07/07/2023] [Indexed: 12/07/2023] Open
Abstract
Background Among the brain-machine interfaces, neurofeedback is a non-invasive technique that uses sensorimotor rhythm (SMR) as a clinical intervention protocol. This study aimed to investigate the clinical applications of SMR neurofeedback to understand its clinical effectiveness in different pathologies or symptoms. Methods A systematic review study with meta-analysis of the clinical applications of EEG-based SMR neurofeedback performed using pre-selected publication databases. A qualitative analysis of these studies was performed using the Consensus tool on the Reporting and Experimental Design of Neurofeedback studies (CRED-nf). The Meta-analysis of clinical efficacy was carried out using Review Manager software, version 5.4.1 (RevMan 5; Cochrane Collaboration, Oxford, UK). Results The qualitative analysis includes 44 studies, of which only 27 studies had some kind of control condition, five studies were double-blinded, and only three reported a blind follow-up throughout the intervention. The meta-analysis included a total sample of 203 individuals between stroke and fibromyalgia. Studies on multiple sclerosis, insomnia, quadriplegia, paraplegia, and mild cognitive impairment were excluded due to the absence of a control group or results based only on post-intervention scales. Statistical analysis indicated that stroke patients did not benefit from neurofeedback interventions when compared to other therapies (Std. mean. dif. 0.31, 95% CI 0.03-0.60, p = 0.03), and there was no significant heterogeneity among stroke studies, classified as moderate I2 = 46% p-value = 0.06. Patients diagnosed with fibromyalgia showed, by means of quantitative analysis, a better benefit for the group that used neurofeedback (Std. mean. dif. -0.73, 95% CI -1.22 to -0.24, p = 0.001). Thus, on performing the pooled analysis between conditions, no significant differences were observed between the neurofeedback intervention and standard therapy (0.05, CI 95%, -0.20 to -0.30, p = 0.69), with the presence of substantial heterogeneity I2 = 92.2%, p-value < 0.001. Conclusion We conclude that although neurofeedback based on electrophysiological patterns of SMR contemplates the interest of numerous researchers and the existence of research that presents promising results, it is currently not possible to point out the clinical benefits of the technique as a form of clinical intervention. Therefore, it is necessary to develop more robust studies with a greater sample of a more rigorous methodology to understand the benefits that the technique can provide to the population.
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Affiliation(s)
- Tatiana Ferri Ribeiro
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Marcelo Alves Carriello
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Eugenio Pereira de Paula
- Physical Education (UFPR)—Invited Colaborador, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Amanda Carvalho Garcia
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Guilherme Luiz da Rocha
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
| | - Helio Afonso Ghizoni Teive
- Internal Medicine and Health Sciences, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
- Department of Clinical Medicine, UFPR, and Coordinator of the Movement Disorders Sector, Neurology Service, Clinic Hospital, Federal University of Paraná (UFPR), Curitiba, Paraná, Brazil
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Yadav H, Maini S. Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-45. [PMID: 37362726 PMCID: PMC10157593 DOI: 10.1007/s11042-023-15653-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 07/17/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.
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Affiliation(s)
- Hitesh Yadav
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
| | - Surita Maini
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
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Wang F, Wen Y, Bi J, Li H, Sun J. A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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Zhang B, Zhou Z, Jiang J. A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli. Brain Sci 2020; 10:brainsci10080524. [PMID: 32781712 PMCID: PMC7464701 DOI: 10.3390/brainsci10080524] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 11/16/2022] Open
Abstract
To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.
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Affiliation(s)
- Boyang Zhang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China;
| | - Zongtan Zhou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China;
- Correspondence: ; Tel.: +86-159-7313-4693
| | - Jing Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China;
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Si-Mohammed H, Petit J, Jeunet C, Argelaguet F, Spindler F, Evain A, Roussel N, Casiez G, Lecuyer A. Towards BCI-Based Interfaces for Augmented Reality: Feasibility, Design and Evaluation. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020; 26:1608-1621. [PMID: 30295623 DOI: 10.1109/tvcg.2018.2873737] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms. In this paper we study the combination of BCI and Augmented Reality (AR). We first tested the feasibility of using BCI in AR settings based on Optical See-Through Head-Mounted Displays (OST-HMDs). Experimental results showed that a BCI and an OST-HMD equipment (EEG headset and Hololens in our case) are well compatible and that small movements of the head can be tolerated when using the BCI. Second, we introduced a design space for command display strategies based on BCI in AR, when exploiting a famous brain pattern called Steady-State Visually Evoked Potential (SSVEP). Our design space relies on five dimensions concerning the visual layout of the BCI menu; namely: orientation, frame-of-reference, anchorage, size and explicitness. We implemented various BCI-based display strategies and tested them within the context of mobile robot control in AR. Our findings were finally integrated within an operational prototype based on a real mobile robot that is controlled in AR using a BCI and a HoloLens headset. Taken together our results (4 user studies) and our methodology could pave the way to future interaction schemes in Augmented Reality exploiting 3D User Interfaces based on brain activity and BCIs.
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Yuan W, Li Z. Brain Teleoperation Control of a Nonholonomic Mobile Robot Using Quadrupole Potential Function. IEEE Trans Cogn Dev Syst 2019. [DOI: 10.1109/tcds.2018.2869903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ruggiero F, Ferrucci R, Bocci T, Nigro M, Vergari M, Marceglia S, Barbieri S, Priori A. Spino-cerebellar tDCS modulates N100 components of the P300 event related potential. Neuropsychologia 2019; 135:107231. [PMID: 31628894 DOI: 10.1016/j.neuropsychologia.2019.107231] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/02/2019] [Accepted: 10/11/2019] [Indexed: 01/01/2023]
Abstract
OBJECTIVE To evaluate the role of the cerebellum and spinal cord in cognitive processes, we assessed changes in event-related potentials (ERPs), before and after different combinations of spinal and cerebellar direct current stimulation (tDCS) in healthy subjects. METHOD We enrolled 37 volunteers (11 males and 26 females, aged 20-50 years), who were subsequently randomly assigned to one of four stimulation conditions: i) anodal cerebellar tDCS, with the reference electrode over the right shoulder; ii) anodal spinal tDCS, with the reference electrode over the right shoulder; iii) anodal spinal tDCS with cathodal cerebellar tDCS, and iv) sham stimulation. Stimulation intensity was set at 2 mA and delivered for 20 min. ERPs were assessed in an auditory oddball task before (T0) and 5 (T1) and 30 min (T2) after tDCS offset. RESULTS In condition iii, spino-cerebellar tDCS, the N100 component at T2 increased in amplitude by 60% (p = 0.019), whereas the sham stimulation left the N100 amplitude unchanged (p > 0.05). CONCLUSION The N100 wave reflects pre-attentive processes and correlates with arousal due to a specific stimuli and selective attention. Because spino-cerebellar tDCS induces electric fields in the brainstem, the facilitation of the N100 may be due to the modulation of the reticular formation. Regardless of the underlying mechanism, spino-cerebellar tDCS can help patients with deficits at the pre-attentive or selective attentional level.
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Affiliation(s)
- Fabiana Ruggiero
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurophysiology Unit, Milan, Italy
| | - Roberta Ferrucci
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurophysiology Unit, Milan, Italy; "Aldo Ravelli" Center for Neurotechnology and Experimental Brain Therapeutics, Dipartimento di Scienze della salute, Università degli Studi di Milano, Milan, Italy; III Neurology Clinic, ASST Santi Paolo e Carlo, Milan, Italy
| | - Tommaso Bocci
- "Aldo Ravelli" Center for Neurotechnology and Experimental Brain Therapeutics, Dipartimento di Scienze della salute, Università degli Studi di Milano, Milan, Italy; III Neurology Clinic, ASST Santi Paolo e Carlo, Milan, Italy
| | - Martina Nigro
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurophysiology Unit, Milan, Italy
| | - Maurizio Vergari
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurophysiology Unit, Milan, Italy
| | - Sara Marceglia
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurophysiology Unit, Milan, Italy; Dipartimento di Ingegneria e Architettura, University of Trieste, Trieste, Italy
| | - Sergio Barbieri
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Neurophysiology Unit, Milan, Italy
| | - Alberto Priori
- "Aldo Ravelli" Center for Neurotechnology and Experimental Brain Therapeutics, Dipartimento di Scienze della salute, Università degli Studi di Milano, Milan, Italy; III Neurology Clinic, ASST Santi Paolo e Carlo, Milan, Italy.
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Kim HH, Jeong J. Decoding electroencephalographic signals for direction in brain-computer interface using echo state network and Gaussian readouts. Comput Biol Med 2019; 110:254-264. [PMID: 31233971 DOI: 10.1016/j.compbiomed.2019.05.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 05/31/2019] [Accepted: 05/31/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Noninvasive brain-computer interfaces (BCI) for movement control via an electroencephalogram (EEG) have been extensively investigated. However, most previous studies decoded user intention for movement directions based on sensorimotor rhythms during motor imagery. BCI systems based on mapping imagery movement of body parts (e.g., left or right hands) to movement directions (left or right directional movement of a machine or cursor) are less intuitive and less convenient due to the complex training procedures. Thus, direct decoding methods for detecting user intention about movement directions are urgently needed. METHODS Here, we describe a novel direct decoding method for user intention about the movement directions using the echo state network and Gaussian readouts. Importantly parameters in the network were optimized using the genetic algorithm method to achieve better decoding performance. We tested the decoding performance of this method with four healthy subjects and an inexpensive wireless EEG system containing 14 channels and then compared the performance outcome with that of a conventional machine learning method. RESULTS We showed that this decoding method successfully classified eight directions of intended movement (approximately 95% of an accuracy). CONCLUSIONS We suggest that the echo state network and Gaussian readouts can be a useful decoding method to directly read user intention of movement directions even using an inexpensive and portable EEG system.
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Affiliation(s)
- Hoon-Hee Kim
- Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jaeseung Jeong
- Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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Grice PM, Kemp CC. In-home and remote use of robotic body surrogates by people with profound motor deficits. PLoS One 2019; 14:e0212904. [PMID: 30875377 PMCID: PMC6420017 DOI: 10.1371/journal.pone.0212904] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Accepted: 02/12/2019] [Indexed: 01/17/2023] Open
Abstract
By controlling robots comparable to the human body, people with profound motor deficits could potentially perform a variety of physical tasks for themselves, improving their quality of life. The extent to which this is achievable has been unclear due to the lack of suitable interfaces by which to control robotic body surrogates and a dearth of studies involving substantial numbers of people with profound motor deficits. We developed a novel, web-based augmented reality interface that enables people with profound motor deficits to remotely control a PR2 mobile manipulator from Willow Garage, which is a human-scale, wheeled robot with two arms. We then conducted two studies to investigate the use of robotic body surrogates. In the first study, 15 novice users with profound motor deficits from across the United States controlled a PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a simulated self-care task. Participants achieved clinically meaningful improvements on the ARAT and 12 of 15 participants (80%) successfully completed the simulated self-care task. Participants agreed that the robotic system was easy to use, was useful, and would provide a meaningful improvement in their lives. In the second study, one expert user with profound motor deficits had free use of a PR2 in his home for seven days. He performed a variety of self-care and household tasks, and also used the robot in novel ways. Taking both studies together, our results suggest that people with profound motor deficits can improve their quality of life using robotic body surrogates, and that they can gain benefit with only low-level robot autonomy and without invasive interfaces. However, methods to reduce the rate of errors and increase operational speed merit further investigation.
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Affiliation(s)
- Phillip M. Grice
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Charles C. Kemp
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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Liu X, Liu N, Zhou M, Lu Y, Li F. Bibliometric analysis of global research on the rehabilitation of spinal cord injury in the past two decades. Ther Clin Risk Manag 2018; 15:1-14. [PMID: 30588000 PMCID: PMC6301731 DOI: 10.2147/tcrm.s163881] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Purpose We aimed to build a model to qualitatively and quantitatively evaluate publications of research of spinal cord injury rehabilitation from 1997 to 2016. Methods Data were obtained from the Web of Science Core Collection on October 6, 2017. We conducted a qualitative and quantitative analysis of publication outputs, journals, authors, institutions, countries, cited references, keywords, and terms by bibliometric methods and bibliometric software packages. Results We identified 5,607 publications on rehabilitation of spinal cord injury from 1997 to 2016, and found that the annual publication rate increased with time. The Archives of Physical Medicine and Rehabilitation published the largest number of literature, the most active country was USA, the most active institution was University of Washington, and Post MWM was the leading author. Keyword analysis indicated that life satisfaction, muscle strength, wheelchair training, walking, gait, and others were the hot spots of these research studies, whereas classification, exoskeleton, plasticity, and old adult were research frontiers. Conclusion This bibliometric study revealed that research on rehabilitation of spinal cord injury is a well-developed and promising research field. Global scientific research cooperation is close. However, higher quality research is needed. Our findings provide valuable information for researchers to identify better perspectives and develop the future research direction.
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Affiliation(s)
- Xiaoxie Liu
- Department of Rehabilitation Medicine, Peking University Third Hospital, Beijing, China,
| | - Nan Liu
- Department of Rehabilitation Medicine, Peking University Third Hospital, Beijing, China,
| | - Mouwang Zhou
- Department of Rehabilitation Medicine, Peking University Third Hospital, Beijing, China,
| | - Yao Lu
- Department of Rehabilitation Medicine, Peking University Third Hospital, Beijing, China,
| | - Fang Li
- Department of Rehabilitation Medicine, Peking University Third Hospital, Beijing, China,
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Abu-Alqumsan M, Ebert F, Peer A. Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems. J Neural Eng 2017; 14:036024. [PMID: 28294109 DOI: 10.1088/1741-2552/aa66e0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. APPROACH To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. MAIN RESULTS Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. SIGNIFICANCE Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations.
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Affiliation(s)
- Mohammad Abu-Alqumsan
- Chair of Automatic Control Engineering, Technical University of Munich (TUM), Munich, Germany
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