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This study explores a multiclass BCI system designed to classify multiple MI tasks using a low-cost EEG device.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A BCI system was developed to classify six mental states: resting state, left and right hand movement imagery, tongue movement, and left and right lateral bending, using EEG data collected with the Emotiv EPOC X headset. Seven participants underwent a body awareness training protocol integrating mindfulness and physical exercises to improve MI performance. Machine learning techniques were applied to extract discriminative features from the EEG signals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Post-training assessments indicated modest improvements in participants' MI proficiency. However, classification performance was limited due to inter- and intra-subject signal variability and the technical constraints of the consumer-grade EEG hardware.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>These findings highlight the value of combining user training with MI-based BCIs and the need to optimize signal quality for reliable performance. The results support the feasibility of scalable, multiclass MI paradigms in low-cost, user-centered neurotechnology applications, while pointing to critical areas for future system enhancement.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fninf.2025.1625279","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T05:31:24Z","timestamp":1754976684000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X"],"prefix":"10.3389","volume":"19","author":[{"given":"Paulina","family":"Tarara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iwona","family":"Przyby\u0142","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Sch\u00f6ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artur","family":"Gunia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"100003","DOI":"10.1016\/j.array.2019.100003","article-title":"Signal processing techniques for motor imagery brain computer interface: a review","volume":"2","author":"Aggarwal","year":"2019","journal-title":"Array"},{"key":"B2","first-page":"242","volume-title":"BCIs That Use Steady-State Visual Evoked Potentials or Slow Cortical Potentials","author":"Allison","year":"2012"},{"key":"B3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/IWW-BCI.2017.7858143","article-title":"\u201cTowards sign language recognition using EEG-based motor imagery brain computer interface,\u201d","volume-title":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","author":"AlQattan","year":"2017"},{"key":"B4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/STSIVA.2015.7330403","article-title":"\u201cKernel-based relevant feature extraction to support motor imagery classification,\u201d","volume-title":"2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA)","author":"Arias-Mora","year":"2015"},{"key":"B5","doi-asserted-by":"publisher","first-page":"355","DOI":"10.3109\/17483107.2014.961569","article-title":"Application of BCI systems in neurorehabilitation: a scoping review","volume":"10","author":"Bamdad","year":"2015","journal-title":"Disabil. 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