Comparison of MI-EEG Decoding in Source to Sensor Domain.

Brain-computer interface (BCI) system based on sensorimotor rhythm (SMR) is a more natural brain-computer interaction system. In this paper, we propose a new multi-task motor imagery EEG (MI-EEG) classification framework. Unlike traditional EEG decoding algorithms, we perform the decoding task in the source domain rather than the sensor domain. In the proposed algorithm, we first build a conduction model of the signal using the public ICBM152 head model and the boundary element method (BEM). The sensor domain EEG was then mapped to the selected cortex region using standardized low-resolution electromagnetic tomography (sLORETA) technology, which benefit to address volume conduction effects problem. Finally, the source domain features are extracted and classified by combining FBCSP and simple LDA. The results show that the classification-decoding algorithm performed in the source domain can well solve the classification task of MI-EEG. In addition, we found that the source imaging method can significantly increase the number of available EEG channels, which can be expanded at least double. The preliminary results of this study encourage the implementation of EEG decoding algorithms in the source domain. Clinical Relevance- This confirms that better results can be obtained by performing MI-EEG decoding in the source domain than in the sensor domain.