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Time-frequency Selection in Two Bipolar Channels for Improving the Classification of Motor Imagery EEG

Abstract : Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient = 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.
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https://hal.archives-ouvertes.fr/hal-00737280
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Submitted on : Monday, October 1, 2012 - 2:35:28 PM
Last modification on : Thursday, October 7, 2021 - 10:48:10 AM
Long-term archiving on: : Wednesday, January 2, 2013 - 6:45:10 AM

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Yuan Yang, Sylvain Chevallier, Joe Wiart, Isabelle Bloch. Time-frequency Selection in Two Bipolar Channels for Improving the Classification of Motor Imagery EEG. The proceeding of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12), Aug 2012, San Diego, United States. pp.2744-2747. ⟨hal-00737280⟩

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