Classification of EEG signals by an evolutionary algorithm
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
dc.contributor.author | VEZARD, Laurent | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
hal.structure.identifier | Advanced Learning Evolutionary Algorithms [ALEA] | |
dc.contributor.author | LEGRAND, Pierrick | |
hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
dc.contributor.author | CHAVENT, Marie | |
hal.structure.identifier | Université de Bordeaux Ségalen [Bordeaux 2] | |
dc.contributor.author | FAITA-AINSEBA, Frederique | |
hal.structure.identifier | Université de Bordeaux Ségalen [Bordeaux 2] | |
dc.contributor.author | CLAUZEL, Julien | |
dc.date.issued | 2012-08-27 | |
dc.date.conference | 2012-08-27 | |
dc.description.abstractEn | The 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.iso | en | |
dc.title.en | Classification of EEG signals by an evolutionary algorithm | |
dc.type | Communication dans un congrès | |
dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
bordeaux.conference.title | COMPSTAT 2012 - 20th International Conference on Computational Statistics | |
bordeaux.country | CY | |
bordeaux.conference.city | Limassol | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-00757270 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | non | |
hal.conference.end | 2012-08-31 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-00757270v1 | |
bordeaux.COinS | ctx_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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