Nature-Inspired Algorithms for Selecting EEG Sources for Motor Imagery Based BCI

0444967 - ÚI 2016 RIV CH eng C - Conference Paper (international conference)
Basterrech, S. - Bobrov, P. - Frolov, A. A. - Húsek, Dušan
Nature-Inspired Algorithms for Selecting EEG Sources for Motor Imagery Based BCI.
Artificial Intelligence and Soft Computing. Vol. 2. Cham: Springer, 2015 - (Rutkowski, L.; Korytkowski, M.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.; Zurada, J.), s. 79-90. Lecture Notes in Artificial Intelligence, 9120. ISBN 978-3-319-19368-7. ISSN 0302-9743.
[ICAISC 2015. International Conference on Artificial Intelligence and Soft Computing /14./. Zakopane (PL), 12.06.2015-16.06.2015]
R&D Projects: GA MŠMT ED1.1.00/02.0070
Grant - others:GA MŠk(CZ) EE2.3.30.0055
Institutional support: RVO:67985807
Keywords : brain computer interface * EEG pattern selection * Bayesian classifier * genetic algorithms * simulating annealing
Subject RIV: IN - Informatics, Computer Science

In this article we examine the performance of two well-known metaheuristic techniques (Genetic Algorithm and Simulating Annealing) for selecting the input features of a classifier in a BCI system. An important problem of the EEG-based BCI system consists in designing the EEG pattern classifier. The selection of the EEG channels used for building that learning predictor has impact in the classifier performance. We present results of both metaheuristic techniques on real data set when the classifier is a Bayesian predictor. We statistically compare that performances with a random selection of the EEG channels. According our empirical results our approach significantly increases the accuracy of the learning predictor.
Permanent Link: http://hdl.handle.net/11104/0247398