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 proceedingChapter

    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 languageEnglish
    Title of host publicationSwarm Intelligence for Electric and Electronic Engineering
    PublisherIGI Global
    Pages326-344
    Number of pages19
    ISBN (Print)9781466626669
    DOIs
    Publication statusPublished - 2012

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  • Cite this

    Atyabi, A., Luerssen, M., Fitzgibbon, S., & Powers, D. (2012). The use of Evolutionary Algorithm-based methods in EEG based BCI systems. In Swarm Intelligence for Electric and Electronic Engineering (pp. 326-344). IGI Global. https://doi.org/10.4018/978-1-4666-2666-9.ch016