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

10/29/2020

∙

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 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.

READ FULL TEXT

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

Evaluation of Motor Imagery-Based BCI methods in neurorehabilitation of Parkinson's Disease patients

End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces

Minimizing subject-dependent calibration for BCI with Riemannian transfer learning

A Novel Framework for Visual Motion Imagery Classification Using 3D Virtual BCI Platform

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages

Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework

Electrostimulation of Brain Deep Structures in Parkinson's Disease

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

Related Research

Evaluation of Motor Imagery-Based BCI methods in neurorehabilitation of Parkinson's Disease patients

End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces

Minimizing subject-dependent calibration for BCI with Riemannian transfer learning

A Novel Framework for Visual Motion Imagery Classification Using 3D Virtual BCI Platform

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages

Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework

Electrostimulation of Brain Deep Structures in Parkinson's Disease