Decoding of motor imagery EEG based on brain source estimation

• Published in: Neurocomputing (Amsterdam) 2019-04, Vol.339, p.182-193
• Main Authors: Li, Ming-Ai, Wang, Yi-Fan, Jia, Song-Min, Sun, Yan-Jun, Yang, Jin-Fu
• Format: Article
• Language: English
• Subjects: Dipole source estimation; MI-EEG; Overlapping averaging; Source decoding; Time of interest; Weighted minimum norm estimate
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.02.006

•A novel Brain Source Estimation, denoted as OA-WMNE, is proposed to decode MI-EEG effectively in the source domain.•The Overlapping Averaging retains complete information of MI-EEG and leads to better spatial separability of dipoles.•The depth-weighted matrix in WMNE can compensate and improve the exact estimation for deep dipoles relative to MNE.•TOI selection overcomes the limitation of the data type and increases the universality of the source decoding.•OA-WMNE enhances the spatial resolution of MI-EEG and reaches the highest average classification accuracy of 81.32%. 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