EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface

Bertrand Rivet (1) , Hubert Cecotti (1) , Ronald Phlypo (1) , Olivier Bertrand (2) , Emmanuel Maby (2) , Jérémie Mattout (2)

1 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
2 Dynamique Cérébrale et Cognition

A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory. We propose a new algorithm to select a relevant subset of electrodes by estimating sparse spatial filters. A $l_1$-norm penalization term, as an approximation of the $l_0$-norm, is introduced in the xDAWN algorithm, which maximizes the signal to signal-plus-noise ratio. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 10 sensors, the loss in classification accuracy is less than 5\%.

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

Bertrand Rivet, Hubert Cecotti, Ronald Phlypo, Olivier Bertrand, Emmanuel Maby, et al.. EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface. EMBC 2010 - 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2010, Buenos Aires, Argentina. pp.n.c, ⟨10.1109/IEMBS.2010.5626485⟩. ⟨hal-00517388⟩