Decoding Voluntary Movement of Single Hand Based on Analysis of Brain Connectivity by Using EEG Signals - Li, Ting; Xue, Tao; Wang, Baozeng; Zhang, Jinhua (Frontiers in Human Neuroscience, 2018)
Research about decoding neurophysiological signals mainly aims to elucidate the details of human motion control from the perspective of neural activity. We utilized brain connectivity analysis with EEG to propose a brain functional network (BFN) and feature extraction algorithm for voluntary hand movement decoding. Through analysis of the characteristic parameters obtained from BFN, we extracted the most important electrode nodes and frequencies for identifying the direction of hand movement. The results demonstrated that the most sensitive EEG components were in frequencies Delta, Theta, and Gamma1 from electrodes F4, F8, C3, Cz, C4, CP4, T3, and T4. Finally, we formed a model for decoding voluntary movement in right hand based on a hierarchical linear model (HLM). Through a voluntary hand movement experiment in a spiral trajectory, the Poisson coefficient between the measurement trajectory and decoding trajectory was used as a test standard to compare with the traditional multiple linear regression model. It was found that the decoding model based on HLM obtained superior results. This paper contributes a feature extraction method based on brain connectivity analysis, which can mine more comprehensive feature information related to a special mental state. The decoding model based on HLM possesses a strong structure for data manipulation, which constitutes another reason for precise decoding.