Ensemble Learning Methods for Classifying EEG Signals

Bagging, boosting and random subspace are three popular ensemble learning methods, which have already shown effectiveness in many practical classification problems. For electroencephalogram (EEG) signal classification arising in recent brain-computer interface (BCI) research, however, there are almost no reports investigating their feasibilities. This paper systematically evaluates the performance of these three ensemble methods for their new application on EEG signal classification. Experiments are conducted on three BCI subjects with k-nearest neighbor and decision tree as base classifiers. Several valuable conclusions are derived about the feasibility and performance of ensemble methods for classifying EEG signals.

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