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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096572" target="_blank" >RIV/61989100:27240/15:86096572 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/15:00444967
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-19369-4_8" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19369-4_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-19369-4_8" target="_blank" >10.1007/978-3-319-19369-4_8</a>

Result language
angličtina
Original language name
Nature-Inspired Algorithms for Selecting EEG Sources for Motor Imagery Based BCI
Original language description
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 consistsin 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 aBayesian 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. (C) Springer International PublishingSwitzerland 2015.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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