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Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain–Computer Interfaces

Abstract : Keeping a minimal number of channels is essential for designing a portable brain–computer interface system for daily usage. Most existing methods choose key channels based on spatial information without optimization of time segment for classification. This paper proposes a novel subject-specific channel selection method based on a criterion called F score to realize the parameterization of both time segment and channel positions. The F score is a novel simplified measure derived from Fisher's discrimi-nant analysis for evaluating the discriminative power of a group of features. The experimental results on a standard dataset (BCI competition III dataset IVa) show that our method can efficiently reduce the number of channels (from 118 channels to 9 in average) without a decrease in mean classification accuracy. Compared to two state-of-the-art methods in channel selection, our method leads to comparable or even better classification results with less selected channels.
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Contributor : Sylvain Chevallier Connect in order to contact the contributor
Submitted on : Friday, August 5, 2016 - 10:30:15 AM
Last modification on : Wednesday, November 3, 2021 - 6:04:16 AM
Long-term archiving on: : Sunday, November 6, 2016 - 10:20:14 AM


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Yuan Yang, Isabelle Bloch, Sylvain Chevallier, Joe Wiart. Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain–Computer Interfaces. Cognitive Computation, Springer, 2016, 8 (3), pp.505-518. ⟨10.1007/s12559-015-9379-z⟩. ⟨hal-01351620⟩



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