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Motor-Imagery based BCI (MI-BCI) neurorehabilitation can improve locomotorability and reduce the deficit symptoms in Parkinson's Disease patients.Advanced Motor-Imagery BCI methods are needed to overcome the accuracy andtime-related MI BCI calibration challenges in such patients. In this study, weproposed a Multi-session FBCSP (msFBCSP) based on inter-session transferlearning and we investigated its performance compared to the single-sessionbased FBSCP. The main result of this study is the significantly improvedaccuracy obtained by proposed msFBCSP compared to single-session FBCSP in PDpatients (median 81.3%, range 41.2-100.0% vs median 61.1%, range 25.0-100.0%,respectively; p<0.001). In conclusion, this study proposes a transferlearning-based multi-session based FBCSP approach which allowed tosignificantly improve calibration accuracy in MI BCI performed on PD patients.

Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients