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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\u00a0#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\u00a0#2), feedback through passive movement was provided, changing the pedal\u2019s 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\u00a0#1) to 86.74% (Mode\u00a0#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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11517-025-03497-6","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T07:44:23Z","timestamp":1770795863000},"page":"1311-1322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1534-0708","authenticated-orcid":false,"given":"Leticia","family":"Silva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00e9ssica","family":"Lima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denis","family":"Delisle-Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teodiano","family":"Bastos-Filho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"issue":"4","key":"3497_CR1","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/TNSRE.2020.2974056","volume":"28","author":"MA Romero-Laiseca","year":"2020","unstructured":"Romero-Laiseca MA, Delisle-Rodriguez D, Cardoso V, Gurve D, Loterio F, Nascimento JHP, Krishnan S, Frizera-Neto A, Bastos-Filho T (2020) A low-cost lower-limb brain-machine interface triggered by pedaling motor imagery for post-stroke patients rehabilitation. 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