Subject-specific channel selection for classification of morot imagery electroencephalographic data

Abstract :

Brain-computer interfaces (BCIs) are systems that record brain signals and then classify them to generate computer commands. Keeping a minimal number of channels (electrodes) is essential for developing portable BCIs. Unlike existing methods choosing channels without optimization of time segment for classification, this work proposes a novel subject-specific channel selection method based on a criterion derived from Fisher’s discriminant analysis to realize the parametrization of both time segment and channel positions. The experimental results show that the method can efficiently reduce the number of channels (from 118 channels to no more than 11), and shorten the training time, without a significant decrease of classification accuracy on a standard dataset.

Document type :
Conference papers
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https://hal.telecom-paristech.fr/hal-02286514
Contributor : Telecomparis Hal <>
Submitted on : Friday, September 13, 2019 - 3:51:05 PM
Last modification on : Thursday, October 17, 2019 - 12:37:01 PM

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  • HAL Id : hal-02286514, version 1

Citation

Yuan Yang, Olexiy Kyrgyzov, Joe Wiart, Isabelle Bloch. Subject-specific channel selection for classification of morot imagery electroencephalographic data. IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP, May 2013, Vancouver, Canada. pp.1277-1280. ⟨hal-02286514⟩

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