Effect of feature and channel selection on EEG classification

Publication Type:
Conference Proceeding
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2006, pp. 2171 - 2174
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In this paper, we evaluate the significance of feature and channel selection on EEG classification. The selection process is performed by searching the feature/channel space using genetic algorithm, and evaluating the importance of subsets using a linear support vector machine classifier. Three approaches have been considered: (i) selecting a subset of features that will be used to represent a specified set of channels, (ii) selecting channels that are each represented by a specified set of features, and (iii) selecting individual features from different channels. When applied to a Brain-Computer Interface (BCI) problem, results indicate that improvement in classification accuracy can be achieved by considering the correct combination of channels and features. © 2006 IEEE.
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