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

TL;DR

This study evaluates the performance of Parkinson's disease patients using motor imagery-based BCI systems, comparing different preprocessing and classification methods to identify the most effective approach for neurorehabilitation.

Contribution

It introduces a comparative analysis of SpecCSP, SPoC, and FBCSP methods for MI-BCI in PD patients, highlighting the superior performance of FBCSP.

Findings

FBCSP achieved higher accuracy than SPoC.

Both SPoC and SpecCSP had lower false-positive ratios.

PD patients could operate MI-BCI with reduced accuracy.

Abstract

The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The results showed that FBCSP outperformed SPoC in terms of accuracy, and both SPoC and SpecCSP in terms of the false-positive ratio. The study also demonstrates that PD patients were capable of operating MI-BCI, although with lower accuracy.