An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation

Background: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New method: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results: The classification accuracy of the CBPT-based BCI system was found to be 92.81 ± 2.09% for the healthy experimental group and 84.53 ± 4.58% for the patients’ group. Comparison with existing method: The CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.