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Ensemble learning for classification of motor imagery tasks in multiclass brain computer interfaces

The difficulty to decode brain signals in a reliable way limits practical motor imagery-based brain computer interface (MI-BCI) applications. The aim of this paper is to propose a classification framework that handle spectral, temporal, and spatial characteristics associated with execution of motor imagery tasks, as well as the temporal variability in EEG data. An ensemble learning approach such as stacked generalization is used to combine information coming from multiple sources. The session-to-session performance of the proposed classifier ensemble is evaluated on a multiclass problem posed in the BCI Competition IV dataset 2a. The results yields a higher mean kappa of 0.66 compared to 0.62 from the baseline linear discriminant analysis (LDA). Also, our approach outperforms the winner of the BCI Competition IV dataset 2a and other studies reported in BCI literature.