Investigating the feasibility of combining EEG and EMG for controlling a hybrid human computer interface in patients with spinal cord injury

Objective. Human-computer interfaces (HCI) are potential tools for assisting (movement replacement) and rehabilitating (movement restoration) individuals with spinal cord injury (SCI). HCIs based on electroencephalography (EEG) have limited accuracy and hence control options; this could be improved by exploiting potential residual muscle activity (electromyography, EMG). The study objectives were to determine if combined EEG and EMG improves offline single-trial movement classification. Furthermore, the effect of number of classes and detection latency on the accuracies was investigated. Methods. Ten able-bodied and eight SCI subjects performed elbow flexion/extension at three force levels while EEG and EMG were recorded. Temporal and spectral features were extracted from the EEG and Hudgins time domain features were extracted from the EMG in 1-second time windows. The time window was shifted (200-ms shift) over 5second epochs around the movement onset. Each segment was classified in three scenarios (2, 3 or 7 classes) using linear discriminant analysis. Results. The accuracies obtained with EEG (51.2%) was outperformed by EMG (95.5%) and combined EMG and EEG (96.2%). Immediately after the EMG onset, the accuracies increased and rapidly reached a plateau. High accuracies were obtained for the different number of classes. Conclusion and Significance. EMG was crucial for obtaining high accuracies, and potential residual EMG should be exploited in HCIs to improve the performance. Force proved to be a viable option for SCI subjects with residual EMG to increase the number of classes for HCI control. These findings could assist design considerations of HCIs for SCI individuals.