Abstract:
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.
