A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients.

This study proposes a novel two-step calibration strategy in a Motor Imagery (MI)-based Brain-Computer Interface (BCI) system for lower-limb post-stroke patients rehabilitation, using electroencephalography (EEG) signals, a virtual reality serious game, and a pedal end-effector. This research proposes a novel MI feature extraction algorithm combining EEG data from pedaling MI and actual movements using k-Nearest Neighbors (k-NN) and probability analysis. The extracted features are used to re-calibrate the BCI system, and improve its accuracy. Initially, participants performed 20 MI trials without feedback (open-loop, Calibration Mode #1) while receiving passive movement. During this phase, EEG data were processed using the Riemannian Geometry method to train a Linear Discriminant Analysis (LDA) classifier. In the second step (closed-loop, Calibration Mode #2), feedback through passive movement was provided, changing the pedal’s speed according to the MI classification consistency. A chronic post-stroke patient tested the proposed BCI system, first receiving transcranial Direct Current Stimulation (tDCS) before each session. The calibration strategy improved classification accuracy from 52.60% (Mode #1) to 86.74% (Mode #2). The classification remained effective throughout sessions, allowing for immediate feedback-driven training while collecting more reliable EEG MI data. The BCI developed here is able to provide post-stroke patient rehabilitation by engaging lower-limb central and peripheral mechanisms through simultaneous MI and passive pedaling during extended workout sessions.