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Classification of EEG signals by an evolutionary algorithm

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorVEZARD, Laurent
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
dc.contributor.authorLEGRAND, Pierrick
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierUniversité de Bordeaux Ségalen [Bordeaux 2]
dc.contributor.authorFAITA-AINSEBA, Frederique
hal.structure.identifierUniversité de Bordeaux Ségalen [Bordeaux 2]
dc.contributor.authorCLAUZEL, Julien
dc.date.issued2012-08-27
dc.date.conference2012-08-27
dc.description.abstractEnThe goal is to predict the alertness of an individual by analyzing the brain activity through electroencephalographic data (EEG) captured with 58 electrodes. Alertness is characterized as a binary variable that can be in a normal or relaxed state. We collected data from 44 subjects before and after a relaxation practice, giving a total of 88 records. After a pre-processing step and data validation, we analyzed each record and discriminate the alertness states using our proposed slope criterion. Afterwards, several common methods for supervised classification (k nearest neighbors, decision trees -CART-, random forests, PLS and discriminant sparse PLS) were applied as predictors for the state of alertness of each subject. The proposed slope criterion was further refined using a genetic algorithm to select the most important EEG electrodes in terms of classification accuracy. Results shown that the proposed strategy derives accurate predictive models of alertness.
dc.language.isoen
dc.title.enClassification of EEG signals by an evolutionary algorithm
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
bordeaux.conference.titleCOMPSTAT 2012 - 20th International Conference on Computational Statistics
bordeaux.countryCY
bordeaux.conference.cityLimassol
bordeaux.peerReviewedoui
hal.identifierhal-00757270
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.end2012-08-31
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00757270v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2012-08-27&rft.au=VEZARD,%20Laurent&LEGRAND,%20Pierrick&CHAVENT,%20Marie&FAITA-AINSEBA,%20Frederique&CLAUZEL,%20Julien&rft.genre=unknown


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