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Subject-specific channel selection for classification of motor 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.
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Contributor : Yuan Yang <>
Submitted on : Tuesday, June 25, 2013 - 4:18:19 PM
Last modification on : Monday, October 12, 2020 - 6:40:59 PM
Long-term archiving on: : Wednesday, April 5, 2017 - 3:37:01 AM

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Yuan Yang, Olexiy Kyrgzov, Joe Wiart, Isabelle Bloch. Subject-specific channel selection for classification of motor imagery electroencephalographic data. 38th International Conference on Acoustics, Speech, and Signal Processing, May 2013, Vancouver, Canada. pp.1277-1280. ⟨hal-00837516⟩

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