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Detecting mental states of alertness with genetic algorithm variable selection

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.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierUniversité Bordeaux Segalen - Bordeaux 2
dc.contributor.authorFAITA-AINSEBA, Frederique
hal.structure.identifierInstituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]
dc.contributor.authorTRUJILLO, Leonardo
dc.date.issued2013-06-20
dc.date.conference2013-06-20
dc.description.abstractEnEmail Print Request Permissions Save to Project The objective of the present work is to develop a method able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state. For instance, pilots or medical staffs are expected to be in a highly alert state, and this method could help to detect possible problems. In this paper, an approach is developed to predict the state of alertness ("normal" or "relaxed") from the study of electroencephalographic signals (EEG) collected with a limited number of electrodes. The EEG of 58 participants in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select an optimal subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the participants. Different subset sizes were investigated and the best result was obtained by considering 9 electrodes (correct classification rate of 73.68%).
dc.language.isoen
dc.title.enDetecting mental states of alertness with genetic algorithm variable selection
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.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
dc.subject.halSciences cognitives/Neurosciences
bordeaux.page1247 - 1254
bordeaux.conference.titleIEEE Congress on Evolutionary Computation (CEC) 2013
bordeaux.countryMX
bordeaux.conference.cityCancun
bordeaux.peerReviewedoui
hal.identifierhal-00939851
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00939851v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2013-06-20&rft.spage=1247%20-%201254&rft.epage=1247%20-%201254&rft.au=VEZARD,%20Laurent&LEGRAND,%20Pierrick&CHAVENT,%20Marie&FAITA-AINSEBA,%20Frederique&TRUJILLO,%20Leonardo&rft.genre=unknown


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