A Region of Interest-Based Electrophysiological Source Imaging Technology and its Applications in Analysis of Motor Imagery EEG Signals

Electrophysiological source imaging (ESI) technology can map scalp potentials to the cerebral cortex, effectively address the shortcomings of low spatial resolution and the influence of volume conduction effects of electroencephalogram (EEG) signals. However, this mapping may lead to a significant increase in the amount of data, slow down data processing speed, and affect the real-time performance of the brain-computer interface (BCI) system. To address above issues, this paper proposes a region of interest (ROI) based ESI technology and applies it to the analysis of motor imagery electroencephalogram (MI-EEG) Signals. The proposed MI-EEG signal analysis method based on ROI-ESI technology first utilizes ESI technology to map scalp potential data to the interior of the cerebral cortex and obtain the source time series; Then, an ROI partitioning rule combining sensor position information is proposed to determine the ROI; Finally, feature extraction and classification of the source time series in the ROI are performed using filter bank CSP (FBCSP) and support vector machine (SVM). Experimental results show that the MI-EEG signal analysis method proposed in this paper can not only obtain accurate brain dipole activity information through scalp potential mapping, thereby accurately decoding EEG signals, but also eliminate source sequences unrelated to motor imagination through the division of interest regions, effectively improving signal processing speed, which makes it more suitable for online BCI applications.