Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG.
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.
Télécom ParisTech, CNRS LTCI, and WHIST Lab, Paris, France. yuan.yang at telecom-paristech.fr
Yuan
Y
Yang
Sylvain
S
Chevallier
Joe
J
Wiart
Isabelle
I
Bloch
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D006801:Humans