A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI - Li, Haoyang; Ji, Hongfei; Yu, Jian; Li, Jie; Jin, Lingjing; Liu, Lingyu; Bai, Zhongfei; Ye, Chen (Frontiers in Neuroscience, 2023)
Brain-computer interfaces (BCI) have the potential to provide neurofeedback to stroke patients to help them rehabilitate motor function. However, current brain-computer interfaces often detect only rough motor intentions, ignoring the precise information about the execution of a complex movement. This is mainly because EEG signals do not contain suﬀicient movement execution features. This paper proposes a sequential learning model with a Graph Isomorphic Network (GIN), which takes a sequence of graph-structured data transformed from EEG and EMG. The data of each movement is divided into sub-actions and predicted separately by the model to obtain a sequential motor encoding that can reflect the sequential features of the movements. Through time-based ensemble learning, more accurate prediction results and execution quality scores can be obtained for each movement. The proposed method achieved a classification accuracy of 88.89% on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method of 73.23%. This approach can be used to build a hybrid EEG-EMG brain- computer interface and provide patients with more precise neural feedback to aid their recovery.