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Conf Proc IEEE Eng Med Biol Soc. 2010;2010:5379-82. doi: 10.1109/IEMBS.2010.5626485.

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

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GIPSAlab, CNRS-UMR 5216, Grenoble Institute of Technology, France.


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%.

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