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Abstract <p>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&#xa0;#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&#xa0;#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&#xa0;#1) to 86.74% (Mode&#xa0;#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.</p> Graphical abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Leticia Silva,
  • Jéssica Lima,
  • Denis Delisle-Rodriguez,
  • Teodiano Bastos-Filho

摘要

Abstract

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.

Graphical abstract