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

Yuan Yang (1) , Olexiy Kyrgzov (1) , Joe Wiart (2) , Isabelle Bloch (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 Orange Labs [Issy les Moulineaux]

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.

Mots clés: Brain computer interfaces; electroencephalography; biomedical signal processing; machine learning

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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⟩