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The Use of Evolutionary Algorithm-Based Methods in EEG Based BCI Systems
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Author(s): Adham Atyabi (Flinders University, Australia), Martin Luerssen (Flinders University, Australia), Sean P. Fitzgibbon (Flinders University, Australia)and David M W Powers (Flinders University, Australia)
Copyright: 2013
Pages: 19
Source title:
Swarm Intelligence for Electric and Electronic Engineering
Source Author(s)/Editor(s): Girolamo Fornarelli (Politecnico di Bari, Italy)and Luciano Mescia (Politecnico di Bari, Italy)
DOI: 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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