Detecting mental states of alertness with genetic algorithm variable selection

Published in Conferences Papers
  1. E Galván-López, B Cody-Kenny, L Trujillo and A Kattan. Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity. In 2013 IEEE Congress on Evolutionary Computation (). June 2013, 2972-2979. DOI BibTeX

    	author = "E. Galván-López and B. Cody-Kenny and L. Trujillo and A. Kattan",
    	booktitle = "2013 IEEE Congress on Evolutionary Computation",
    	title = "Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity",
    	year = 2013,
    	volume = "",
    	number = "",
    	pages = "2972-2979",
    	keywords = "genetic algorithms;GP;brute force mechanism;crossover operator;crossover-semantics based operator;genetic programming;selection mechanism;semantic diversity promotion;semantic-based approach;semantics-in-selection method;trial-and-error method;Computational efficiency;Context;Genetic programming;Semantics;Sociology;Statistics;Vectors",
    	doi = "10.1109/CEC.2013.6557931",
    	issn = "1089-778X",
    	month = "June"

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%).

Published in
IEEE Congress on Evolutionary Computation (CEC)
Pages 1247-1254
Date of conference
20-23 June 2013