Classification methods for ongoing EEG and MEG signals

Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (E...

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Autores principales:	BESSERVE,MICHEL, JERBI,KARIM, LAURENT,FRANCOIS, BAILLET,SYLVAIN, MARTINERIE,JACQUES, GARNERO,LINE
Lenguaje:	English
Publicado:	Sociedad de Biología de Chile 2007
Materias:	brain computer interface electroencephalography magnetoencephalography visuomotor control Support Vector Machine
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500005
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spelling	oai:scielo:S0716-976020070005000052008-05-28Classification methods for ongoing EEG and MEG signalsBESSERVE,MICHELJERBI,KARIMLAURENT,FRANCOISBAILLET,SYLVAINMARTINERIE,JACQUESGARNERO,LINE brain computer interface electroencephalography magnetoencephalography visuomotor control Support Vector Machine Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental statesinfo:eu-repo/semantics/openAccessSociedad de Biología de ChileBiological Research v.40 n.4 20072007-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500005en10.4067/S0716-97602007000500005
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	brain computer interface electroencephalography magnetoencephalography visuomotor control Support Vector Machine
spellingShingle	brain computer interface electroencephalography magnetoencephalography visuomotor control Support Vector Machine BESSERVE,MICHEL JERBI,KARIM LAURENT,FRANCOIS BAILLET,SYLVAIN MARTINERIE,JACQUES GARNERO,LINE Classification methods for ongoing EEG and MEG signals
description	Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states
author	BESSERVE,MICHEL JERBI,KARIM LAURENT,FRANCOIS BAILLET,SYLVAIN MARTINERIE,JACQUES GARNERO,LINE
author_facet	BESSERVE,MICHEL JERBI,KARIM LAURENT,FRANCOIS BAILLET,SYLVAIN MARTINERIE,JACQUES GARNERO,LINE
author_sort	BESSERVE,MICHEL
title	Classification methods for ongoing EEG and MEG signals
title_short	Classification methods for ongoing EEG and MEG signals
title_full	Classification methods for ongoing EEG and MEG signals
title_fullStr	Classification methods for ongoing EEG and MEG signals
title_full_unstemmed	Classification methods for ongoing EEG and MEG signals
title_sort	classification methods for ongoing eeg and meg signals
publisher	Sociedad de Biología de Chile
publishDate	2007
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500005
work_keys_str_mv	AT besservemichel classificationmethodsforongoingeegandmegsignals AT jerbikarim classificationmethodsforongoingeegandmegsignals AT laurentfrancois classificationmethodsforongoingeegandmegsignals AT bailletsylvain classificationmethodsforongoingeegandmegsignals AT martineriejacques classificationmethodsforongoingeegandmegsignals AT garneroline classificationmethodsforongoingeegandmegsignals
_version_	1718441423954509824

Classification methods for ongoing EEG and MEG signals

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