Motor imagery performance evaluation using hybrid EEG-NIRS for BCI

In this paper, we have evaluated the performance of motor imagery (MI), before and after training by a rehabilitation robot, for brain-computer interface (BCI). A hybrid electroencephalography and near-infrared spectroscopy (EEG-NIRS) system is used to detect the MI by placing the electrodes and optodes around the motor cortex region. Five healthy subjects have participated in the experiment. The subjects are assisted by a rehabilitation robot in an arm movement paradigm during the training session. The MI activity of the subjects is recorded before and after the training sessions. The brain signals from the motor cortex are recorded simultaneously using EEG-NIRS. We found a significant improvement in the MI performance after training. Linear discriminant analysis is used to classify the acquired activity in an offline analysis. The data analysis shows that the hybrid EEG-NIRS can detect better motor activity than individual modality. The average classification accuracy of the subjects has increased from 66% to 94% after training. We propose that the training of the motor cortex by a rehabilitation robot can improve the MI performance for BCI.