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Detecting mental states of alertness with genetic algorithm variable selection

Laurent Vezard 1, 2 Pierrick Legrand 1, 3 Marie Chavent 1, 2 Frederique Faita-Ainseba 4 Leonardo Trujillo 5
2 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
3 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : 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%).
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Submitted on : Thursday, January 30, 2014 - 11:01:19 PM
Last modification on : Friday, December 3, 2021 - 12:20:06 PM


  • HAL Id : hal-00939851, version 1



Laurent Vezard, Pierrick Legrand, Marie Chavent, Frederique Faita-Ainseba, Leonardo Trujillo. Detecting mental states of alertness with genetic algorithm variable selection. IEEE Congress on Evolutionary Computation (CEC) 2013, Jun 2013, Cancun, Mexico. pp.1247 - 1254. ⟨hal-00939851⟩



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