Detecting mental states of alertness with genetic algorithm variable selection

Published in Conferences Papers
  1. Laurent Vezard, Marie Chavent, Pierrick Legrand, Frédérique Fa\"ıta-A\"ınseba and Leonardo Trujillo. Detecting mental states of alertness with genetic algorithm variable selection. In IEEE Congress on Evolutionary Computation. 2013, 1247-1254. BibTeX

    @inproceedings{DBLP:conf/cec/VezardCLFT13,
    	author = {Laurent Vezard and Marie Chavent and Pierrick Legrand and Fr{\'e}d{\'e}rique Fa\"{\i}ta-A\"{\i}nseba and Leonardo Trujillo},
    	title = "Detecting mental states of alertness with genetic algorithm variable selection",
    	booktitle = "IEEE Congress on Evolutionary Computation",
    	year = 2013,
    	pages = "1247-1254",
    	ee = "http://dx.doi.org/10.1109/CEC.2013.6557708",
    	crossref = "DBLP:conf/cec/2013",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, June 20-23, 2013. IEEE, 2013. BibTeX

    @proceedings{DBLP:conf/cec/2013,
    	title = "Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, June 20-23, 2013",
    	booktitle = "IEEE Congress on Evolutionary Computation",
    	publisher = "IEEE",
    	year = 2013,
    	isbn = "978-1-4799-0452-5, 978-1-4799-0453-2",
    	ee = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6552460",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
Abstract

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
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557708&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6552460%2F6557545%2F06557708.pdf%3Farnumber%3D6557708
Date of conference
20-23 June 2013
E-ISBN
978-1-4799-0452-5
ISBN
978-1-4799-0453-2
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