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

• URL: http://arxiv.org/abs/2010.15899v1
• Date: Thu, 29 Oct 2020 19:28:00 GMT
• Title: Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients
• Authors: Aleksandar Miladinovi\'c, Milo\v{s} Aj\v{c}evi\'c, Pierpaolo Busan, Joanna Jarmolowska, Giulia Silveri, Susanna Mezzarobba, Piero Paolo Battaglini, Agostino Accardo
• Abstract summary: Motor-motorry based BCI (MIBCI) can improve ability and reduce deficit symptoms in Parkinson's Disease patients. Advanced MotorImagery BCI methods are needed to overcome accuracy and time-related calibration challenges. This study proposes a transfer learning-based FBCSP approach which allowed to significantly improve accuracy accuracy in MI BCI performed on PD patients.
• Score: 50.591267188664666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motor-Imagery based BCI (MI-BCI) neurorehabilitation can improve locomotor ability and reduce the deficit symptoms in Parkinson's Disease patients. Advanced Motor-Imagery BCI methods are needed to overcome the accuracy and time-related MI BCI calibration challenges in such patients. In this study, we proposed a Multi-session FBCSP (msFBCSP) based on inter-session transfer learning and we investigated its performance compared to the single-session based FBSCP. The main result of this study is the significantly improved accuracy obtained by proposed msFBCSP compared to single-session FBCSP in PD patients (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 transfer learning-based multi-session based FBCSP approach which allowed to significantly improve calibration accuracy in MI BCI performed on PD patients.

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