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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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 56; H. 8; S. 2035 - 2043
Hauptverfasser:	Rivet, Bertrand, Souloumiac, Antoine, Attina, Virginie, Gibert, Guillaume
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	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
Schlagworte:	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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Abstract	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.
AbstractList	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.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. 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.
Author	Rivet, Bertrand Souloumiac, Antoine Attina, Virginie Gibert, Guillaume
Author_xml	– sequence: 1 givenname: Bertrand surname: Rivet fullname: Rivet, Bertrand email: bertrand.rivet@gipsa-lab.inpg.fr organization: GIPSA Lab., Grenoble Inst. of Technol., Grenoble, France – sequence: 2 givenname: Antoine surname: Souloumiac fullname: Souloumiac, Antoine email: antoine.souloumiac@cea.fr – sequence: 3 givenname: Virginie surname: Attina fullname: Attina, Virginie email: virginie.attina@lyon.inserm.fr – sequence: 4 givenname: Guillaume surname: Gibert fullname: Gibert, Guillaume email: guillaume.gibert@lyon.inserm.fr
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/19174332$$D View this record in MEDLINE/PubMed https://hal.science/hal-00454568$$DView record in HAL
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Snippet	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...
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SubjectTerms	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
Title	xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain-Computer Interface
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