Decoding of motor imagery EEG based on brain source estimation

Abstract

The decoding of Motor Imagery EEG (MI-EEG) is the most crucial part of biosignal processing in the Brain-computer Interface (BCI) system. The traditional recognition mode is always devoted to extracting and classifying the spatiotemporal feature information of MI-EEG in the sensor domain, but these brain dynamic characteristics, which are derived from the cerebral cortical neurons, are reflected more immediately and obviously with high spatial resolution in the source domain. With the development of neuroscience, the state-of-the-art EEG Source Imaging (ESI) technology converts the scalp signals into brain source space and excavates the way for source decoding of MI-EEG. Minimum Norm Estimate (MNE) is a classical and original EEG inverse transformation. Due to the lack of depth weighting of dipoles, it may be more suitable for the estimation of superficial dipoles and will be slightly insufficient for further source classification. In addition, the selection of a Region of Interest (ROI) is usually an essential step in the source decoding of MI-EEG by Independent Component Analysis (ICA), and the most relevant independent component of original EEG signals is transformed into the equivalent current dipoles to obtain the ROI by ESI. Although the excellent results of this method can be obtained for unilateral limb motor imaging EEG signals, which shows more distinct phenomena of event-related desynchronization (ERD), the decoding accuracy may be restricted for more complex multi-limb motor imagery tasks, whose ERD is no longer evident. Therefore, in this paper, we propose a novel brain source estimation to decode MI-EEG by applying Overlapping Averaging (OA) in the temporal domain and Weighted Minimum Norm Estimate (WMNE), which overcomes the limitations of general ROI-based decoding methods and introduces weighting factors to complement the estimation of deep dipoles. Its advantages will be evaluated on a public dataset with five subjects by comparing it with MNE, WMNE, sLORETA, OA-MNE and ICA-WMNE. The proposed method reaches a higher average decoding accuracy of 81.32% compared to other methods by 10-fold cross-validation at the same chance level. This study will increase the universality of the source decoding and facilitate the development of a BCI system in the source domain.