Hybrid EEG-NIRS BCI using Feature Extraction based on Riemannian Geometry

Brain-computer interface (BCI), which enables direct control of computers and external devices through brain activity, holds significant promise for rehabilitating individuals with motor dysfunctions. However, problems such as low accuracy and non-stationarity of physiological signals remain. In this study, we propose a hybrid BCI using electroencephalography (EEG) and near-infrared spectroscopy (NIRS) with a feature extraction method based on Riemannian geometry. Our study analyzed EEG and NIRS data from 29 healthy subjects during motor imagery of left or right hand opening and closing movements. We discuss the effectiveness of the proposed method by discriminant performance.