xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain-Computer Interface

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance...

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Vydané v:IEEE transactions on biomedical engineering Ročník 56; číslo 8; s. 2035 - 2043
Hlavní autori: Rivet, Bertrand, Souloumiac, Antoine, Attina, Virginie, Gibert, Guillaume
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Adult
Algorithms
Application software
Artificial Intelligence
Bayesian methods
Brain
Brain - physiology
Brain computer interfaces
Brain-computer interface (BCI)
Communication system control
Computer interfaces
Computer Science
Control systems
Electroencephalography
Electroencephalography - methods
Engineering Sciences
Evoked Potentials
Evoked Potentials - physiology
Humans
Life Sciences
Linear discriminant analysis
Male
Man-Machine Systems
Neurons and Cognition
P300 speller
Signal and Image processing
Signal Processing, Computer-Assisted
spatial enhancement
Spatial filters
Stochastic processes
xDAWN algorithm
ISSN:0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier, show that the proposed method is efficient and accurate.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2009.2012869