The use of Evolutionary Algorithm-based methods in EEG based BCI systems

Adham Atyabi; Martin Luerssen; Sean Fitzgibbon; David Powers

doi:10.4018/978-1-4666-2666-9.ch016

The use of Evolutionary Algorithm-based methods in EEG based BCI systems

Adham Atyabi, Martin Luerssen, Sean Fitzgibbon, David Powers

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Citations (Scopus)

Abstract

Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brain-waves 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.

Original language	English
Title of host publication	Swarm Intelligence for Electric and Electronic Engineering
Publisher	IGI Global
Pages	326-344
Number of pages	19
ISBN (Print)	9781466626669
DOIs	https://doi.org/10.4018/978-1-4666-2666-9.ch016
Publication status	Published - 2012

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The use of Evolutionary Algorithm-based methods in EEG based BCI systems

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