A Novel Strategy for Differentiating Motor Imagination Brain-Computer Interface Tasks by Fusing EEG and Functional Near-Infrared Spectroscopy Signals

The multimodal brain-computer interface (BCI) is an innovative paradigm for human-computer interaction that utilizes both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals; therefore, it has received considerable interest. In this study, we concurrently collected EEG and fNIRS data from eighteen healthy participants while they engaged in the mental simulation of performing grasping movements with their left and right hands. During the feature screening session, we assessed the effectiveness of combining the Relief and minimum redundancy maximum relevance (mRMR) algorithms. This algorithm was applied individually to analyze the common spatial pattern (CSP) characteristics of EEG signals across distinct frequency bands as well as the modified CSP (MCSP) attributes of fNIRS signals. Moreover, the enhancement of classification accuracy via feature-level fusion of the two signal types was investigated. The support vector machine (SVM) algorithm was used as the classifier for both training and validation. The results show a significant decrease in the feature count and a substantial enhancement in classification accuracy. Additionally, the highest classification accuracy (88.33 % +/- 5.80 % for EEG + HbO + HbR, P < 0.05) was achieved when utilizing multimodal features, which exceeds that when utilizing EEG alone (84.28 % +/- 7.56 %). Furthermore, the group of participants yielding an enhanced classification accuracy under the multi-modal characteristics constituted the highest percentage among all participants in the case of combined EEG and HbR (88.89 %). The proposed multi-modal information fusion strategy can serve as an effective reference for task recognition in BCI.