Performance Investigation of Brain-Computer Interfaces that Combine EEG and fNIRS for Motor Imagery Tasks

Brain-Computer Interfaces (BCI) have proved to be a promising tool for neurorehabilitation. However, BCIs based on conventional methods are not highly accurate and reliable, different brain activity patterns are not optimal for all the users of BCIs and has low information transfer rate. Several studies have shown that the combination of different brain signal acquisition methods can lead to higher performance of BCIs. In this paper, we aim to investigate whether the performance of BCI increases if we combine Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) simultaneously for classifying Motor Imagery (MI) tasks of right- versus left-hand grasping movement. The results show enhancement in classification accuracy using a multimodal approach of an EEG + fNIRS BCI with an average increase of approximately 8-10% compared to only EEG-based BCI. This indicates that the hybrid approach in Brain-Computer Interface is capable of enhancing the BCI performance.