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Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair

The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individu...

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Autores principales: Saichoo, Theerat, Boonbrahm, Poonpong, Punsawad, Yunyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9781917/
https://www.ncbi.nlm.nih.gov/pubmed/36560158
http://dx.doi.org/10.3390/s22249788
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author Saichoo, Theerat
Boonbrahm, Poonpong
Punsawad, Yunyong
author_facet Saichoo, Theerat
Boonbrahm, Poonpong
Punsawad, Yunyong
author_sort Saichoo, Theerat
collection PubMed
description The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.
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spelling pubmed-97819172022-12-24 Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair Saichoo, Theerat Boonbrahm, Poonpong Punsawad, Yunyong Sensors (Basel) Article The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities. MDPI 2022-12-13 /pmc/articles/PMC9781917/ /pubmed/36560158 http://dx.doi.org/10.3390/s22249788 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Saichoo, Theerat
Boonbrahm, Poonpong
Punsawad, Yunyong
Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
title Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
title_full Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
title_fullStr Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
title_full_unstemmed Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
title_short Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
title_sort investigating user proficiency of motor imagery for eeg-based bci system to control simulated wheelchair
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9781917/
https://www.ncbi.nlm.nih.gov/pubmed/36560158
http://dx.doi.org/10.3390/s22249788
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