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• W2006734965 abstract "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 <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm penalization term, as an approximation of the l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -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%." @default.
• W2006734965 created "2016-06-24" @default.
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• W2006734965 date "2010-08-01" @default.
• W2006734965 modified "2023-09-25" @default.
• W2006734965 title "EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface" @default.
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• W2006734965 doi "https://doi.org/10.1109/iembs.2010.5626485" @default.
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