An experimental evaluation of ensemble methods for EEG signal classification

Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain–computer interfaces (BCIs), there are almost no studies investigating their feasibilities. The present study systematically evaluates the performance of the three ensemble methods for EEG signal classification of mental imagery tasks. With the base classifiers of k-nearest-neighbor, decision tree and support vector machine, classification experiments are carried out upon real EEG recordings. Experimental results suggest the feasibilities of ensemble classification methods, and we also derive some valuable conclusions on the performance of ensemble methods for EEG signal classification.