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A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data...

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Autores principales: Chen, Yan, Hang, Wenlong, Liang, Shuang, Liu, Xuejun, Li, Guanglin, Wang, Qiong, Qin, Jing, Choi, Kup-Sze
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719793/
https://www.ncbi.nlm.nih.gov/pubmed/33328874
http://dx.doi.org/10.3389/fnins.2020.606949
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author Chen, Yan
Hang, Wenlong
Liang, Shuang
Liu, Xuejun
Li, Guanglin
Wang, Qiong
Qin, Jing
Choi, Kup-Sze
author_facet Chen, Yan
Hang, Wenlong
Liang, Shuang
Liu, Xuejun
Li, Guanglin
Wang, Qiong
Qin, Jing
Choi, Kup-Sze
author_sort Chen, Yan
collection PubMed
description In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.
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spelling pubmed-77197932020-12-15 A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface Chen, Yan Hang, Wenlong Liang, Shuang Liu, Xuejun Li, Guanglin Wang, Qiong Qin, Jing Choi, Kup-Sze Front Neurosci Neuroscience In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient. Frontiers Media S.A. 2020-11-23 /pmc/articles/PMC7719793/ /pubmed/33328874 http://dx.doi.org/10.3389/fnins.2020.606949 Text en Copyright © 2020 Chen, Hang, Liang, Liu, Li, Wang, Qin and Choi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Chen, Yan
Hang, Wenlong
Liang, Shuang
Liu, Xuejun
Li, Guanglin
Wang, Qiong
Qin, Jing
Choi, Kup-Sze
A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
title A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
title_full A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
title_fullStr A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
title_full_unstemmed A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
title_short A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
title_sort novel transfer support matrix machine for motor imagery-based brain computer interface
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719793/
https://www.ncbi.nlm.nih.gov/pubmed/33328874
http://dx.doi.org/10.3389/fnins.2020.606949
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