An EEG Source Imaging-based Feature Extraction Method for Motor Imagery Classification

• Published in: Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 1648 - 1652
• Main Authors: Li, Junhan, Zheng, Nengheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.10.2022
• Subjects: EEG source imaging; Electroencephalogram; Electroencephalography; Event-Related Desynchronization; Feature extraction; Filtering algorithms; Image recognition; Imaging; Linearly Constrained Minimum Variance; Motor Imagery; Signal processing; Signal processing algorithms
• ISSN: 2577-1655
• DOI: 10.1109/SMC53654.2022.9945567

This paper presents a new feature extraction method for Electroencephalogram (EEG)-based motor imagery (MI) classification. Current researches mostly classify different MIs by detecting the event-related desynchronization (ERD) phenomenon from the EEG signals. Due to the poor spatial resolution of the MI-EEG signals, the cortical area (source) activating the MI cannot be located accurately with the EEG (sensor) signal, which might degrade the classification accuracy. This study adopts the EEG source imaging (ESI) technique to estimate the cortical area where source ERD happens from the EEG signal. An improved ESI method based on the linearly constrained minimum variance (LCMV) algorithm, in which an average LCMV filter and an average baseline covariance are constructed for the ESI, is proposed to locate the activated cortical area from the noisy EEG signals. The source ERD features are then extracted. Analytical results show that, for subjects with obvious average source ERD phenomenon, their activated cortical area in a single-trial MI can be well located. MI classification results also support the feasibility of the proposed method for MI-EEG signal processing.