Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand

Advances in intelligent systems and computingPublished 1 January 2013

Izabela Rejer

Citations38

SNIP0.30

TL;DR

A modified version of genetic algorithm is presented, capable of choosing feature sets of a slightly lower classification precision but significantly smaller number of features, which is presented via a benchmark EEG set submitted to the second BCI Competition.

Abstract

The crucial problem which has to be solved when an effective brain-computer interface (BCI) is to be design is: how to reduce the huge space of features extracted from raw EEG signals? One of the techniques of feature selection often used by BCI researches are genetic algorithms (GA). This approach, in its classic form, allows obtaining a feature set which gives the high classification precision, however, the dimension of this set is often still too large to create a reliable classifier. The paper presents a modified version of genetic algorithm, which is capable of choosing feature sets of a slightly lower classification precision but significantly smaller number of features. The practical application of the proposed algorithm will be presented via a benchmark EEG set submitted to the second BCI Competition (data set III - motor imaginary).

Keywords

Computer ScienceNeuroscience

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