A multimodal fNIRS and EEG-based BCI study on motor imagery and passive movement

Abstract

In EEG-based motor imagery Brain-Computer interface (BCI), EEG data collected in the calibration phase is used as a subject-specific model to classify the EEG data in the evaluation phase. Previous study has shown the feasibility of calibrating EEG-based BCI from passive movement. This paper investigates the primary sensorimotor area activation from fNIRS on 4 subjects using multimodal NIRS and EEG-based BCI system while performing motor imagery and passive movement of the hand by a Haptic Knob robot. NIRS_SPM is used to compute the changes in hemoglobin response and to generate brain activation map based on the contrasts of motor imagery versus idle and passive movement versus idle. The results on the contrasts showed that passive movement versus idle yielded significant differences compared to motor imagery versus idle. In addition, the results of classifying the NIRS and EEG data separately also showed that the accuracies on classifying passive movement versus idle are better than that of motor imagery versus idle. The results suggest a potential of using passive movement data to calibrate motor imagery in a multimodal NIRS and EEG-based BCI.