VEZARD, Laurent; LEGRAND, Pierrick; CHAVENT, Marie; FAITA-AINSEBA, Frederique; TRUJILLO, Leonardo

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VEZARD, Laurent
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]

LEGRAND, Pierrick
Institut de Mathématiques de Bordeaux [IMB]
Advanced Learning Evolutionary Algorithms [ALEA]

CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]

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Communication dans un congrès

Ce document a été publié dans

IEEE Congress on Evolutionary Computation (CEC) 2013, 2013-06-20, Cancun. 2013-06-20p. 1247 - 1254

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Email 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%).< Réduire

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Institut de Mathématiques de Bordeaux (IMB) - UMR 5251