Heterogeneous classifier ensembles for EEG-based motor imaginary detection

EEG signal classification is a challenging task in that the nature of the EEG data may vary from subject to subject, and change over time for the same subject. To improve classification performance, we propose to construct heterogeneous classifier ensembles, where not only the base classifiers are of different types, but they have different input features as well. The classification performance of the proposed method has been examined on Berlin BCI competition III datasets IVa. Our comparative results clearly show that heterogeneous ensembles outperform single models as well as ensembles having the same input features.