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Open AccessArticle
EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier
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Department of System Engineering and Automation, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Leganes, Spain
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Department of Signal Theory and Communications, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Leganes, Spain
3
Electronic and Computer Science Department, Faculty of Engineering, Mondragon Unibertsitatea, 20500 Arrasate, Spain
*
Author to whom correspondence should be addressed.
Biomimetics 2024, 9(8), 459; https://doi.org/10.3390/biomimetics9080459 (registering DOI)
Submission received: 17 April 2024
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Revised: 2 July 2024
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Accepted: 21 July 2024
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Published: 27 July 2024
Abstract
Individuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain–computer interface (BCI) technology has emerged as a promising solution by offering innovative interaction methods and intelligent rehabilitation training. By leveraging electroencephalographic (EEG) signals, BCIs unlock intriguing possibilities in patient care and neurological rehabilitation. Recent research has utilized covariance matrices as signal descriptors. In this study, we introduce two methodologies for covariance matrix analysis: multiple tangent space projections (M-TSPs) and Cholesky decomposition. Both approaches incorporate a classifier that integrates linear and nonlinear features, resulting in a significant enhancement in classification accuracy, as evidenced by meticulous experimental evaluations. The M-TSP method demonstrates superior performance with an average accuracy improvement of 6.79% over Cholesky decomposition. Additionally, a gender-based analysis reveals a preference for men in the obtained results, with an average improvement of 9.16% over women. These findings underscore the potential of our methodologies to improve BCI performance and highlight gender-specific performance differences to be examined further in our future studies.
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MDPI and ACS Style
Omari, S.; Omari, A.; Abu-Dakka, F.; Abderrahim, M.
EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier. Biomimetics 2024, 9, 459.
https://doi.org/10.3390/biomimetics9080459
AMA Style
Omari S, Omari A, Abu-Dakka F, Abderrahim M.
EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier. Biomimetics. 2024; 9(8):459.
https://doi.org/10.3390/biomimetics9080459
Chicago/Turabian Style
Omari, Sara, Adil Omari, Fares Abu-Dakka, and Mohamed Abderrahim.
2024. "EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier" Biomimetics 9, no. 8: 459.
https://doi.org/10.3390/biomimetics9080459
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