EEG Motor Imagery Decoding Based on Common Spatial Pattern and Ensemble Learning at the Source Space

Abstract

Due to the volume conduct effect, the spatial resolution of electroencephalography (EEG) is low, leading to the limited motor imagery (MI) decoding accuracy based on the scalp EEG signals. In this work, we propose a new MI decoding method based on the EEG source imaging(ESI). Specifically, we first divide the cortical motor areas into several regions of interest (ROIs) using the multi-source pre-registration method. Subsequently, we employ the ESI algorithms to reconstruct the cortical sources within the motor areas, obtaining much higher spatial resolution information for MI decoding. The common spatial pattern (CSP) is then applied to each ROI. Using cross-validation, twenty ROIs contributing to the MI decoding the most are selected. Finally, the decoding decision is obtained based on the ensemble decision of the selected ROIs. Experiments results based on two public datasets show the superior MI decoding performance in the source space than that in the sensor space.