{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:10:52Z","timestamp":1790633452760,"version":"4.1.0"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019347","name":"Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia","doi-asserted-by":"crossref","award":["SGI 3904"],"award-info":[{"award-number":["SGI 3904"]}],"id":[{"id":"10.13039\/100019347","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advances in brain\u2013computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky\u2013Golay filtering with a Random Forest classifier, reaching 87.36% \u00b1 4% accuracy and an F1-score of 87.18% \u00b1 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments.<\/jats:p>","DOI":"10.3390\/s25206387","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T10:28:36Z","timestamp":1760696916000},"page":"6387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based Pattern Detection"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1873-8136","authenticated-orcid":false,"given":"Sonia Roc\u00edo","family":"Moreno-Castelblanco","sequence":"first","affiliation":[{"name":"Software Research Group, Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia, Tunja 150002, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-1742","authenticated-orcid":false,"given":"Manuel Andr\u00e9s","family":"V\u00e9lez-Guerrero","sequence":"additional","affiliation":[{"name":"Software Research Group, Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia, Tunja 150002, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9894-8737","authenticated-orcid":false,"given":"Mauro","family":"Callejas-Cuervo","sequence":"additional","affiliation":[{"name":"Software Research Group, Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia, Tunja 150002, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3390\/signals4010004","article-title":"A Review of Online Classification Performance in Motor Imagery-Based Brain\u2013Computer Interfaces for Stroke Neurorehabilitation","volume":"4","author":"Vavoulis","year":"2023","journal-title":"Signals"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/RBME.2024.3449790","article-title":"Non-Invasive Brain-Computer Interfaces: State of the Art and Trends","volume":"18","author":"Edelman","year":"2025","journal-title":"IEEE Rev. 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