Ping-Ju Lin, Wei Li, Xiaoxue Zhai, Zhibin Li, Jingyao Sun, Quan Xu, Yu Pan, Linhong Ji, Chong Li
Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction...
April 5, 2024: IEEE Transactions on Neural Systems and Rehabilitation Engineering