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The Use of Evolutionary Algorithm-Based Methods in EEG Based BCI Systems

The Use of Evolutionary Algorithm-Based Methods in EEG Based BCI Systems

Adham Atyabi, Martin Luerssen, Sean P. Fitzgibbon, David M W Powers
Copyright: © 2013 |Pages: 19
ISBN13: 9781466626669|ISBN10: 1466626666|EISBN13: 9781466626973
DOI: 10.4018/978-1-4666-2666-9.ch016
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MLA

Atyabi, Adham, et al. "The Use of Evolutionary Algorithm-Based Methods in EEG Based BCI Systems." Swarm Intelligence for Electric and Electronic Engineering, edited by Girolamo Fornarelli and Luciano Mescia, IGI Global, 2013, pp. 326-344. https://doi.org/10.4018/978-1-4666-2666-9.ch016

APA

Atyabi, A., Luerssen, M., Fitzgibbon, S. P., & Powers, D. M. (2013). The Use of Evolutionary Algorithm-Based Methods in EEG Based BCI Systems. In G. Fornarelli & L. Mescia (Eds.), Swarm Intelligence for Electric and Electronic Engineering (pp. 326-344). IGI Global. https://doi.org/10.4018/978-1-4666-2666-9.ch016

Chicago

Atyabi, Adham, et al. "The Use of Evolutionary Algorithm-Based Methods in EEG Based BCI Systems." In Swarm Intelligence for Electric and Electronic Engineering, edited by Girolamo Fornarelli and Luciano Mescia, 326-344. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2666-9.ch016

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Abstract

Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brainwaves recorded from the scalp as a means for providing a new communication channel by which people with limited physical communication capability can effect control over devices such as moving a mouse and typing characters. Evolutionary approaches have the potential to improve the performance of such system through providing a better sub-set of electrodes or features, reducing the required training time of the classifiers, reducing the noise to signal ratio, and so on. This chapter provides a survey on some of the commonly used EA methods in EEG study.

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