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Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . While virtual reality (VR) combined with action observation (AO) provides enriched visual guidance for motor imagery (MI), the decoding stability of MI Brain\u2013Computer Interface (BCI) remains a challenge due to the inherent non-stationarity and low signal-to-noise ratio of Electroencephalography (EEG) signals in dynamic environments. Enhancing decoding accuracy and understanding neural representations in VR-based AO + MI tasks remain important challenges in neural engineering.\n                    <jats:italic>Approach.<\/jats:italic>\n                    This study proposes a hierarchical graph learning framework tailored for VR-based MI decoding, leveraging multivariate variational mode decomposition to decouple complex EEG signals into rhythmic components for enhanced multi-domain feature analysis, hybrid feature selection for compact representation, and functional brain network modeling based on phase locking value. Each EEG trial is represented as a sparsified graph, and a hierarchical graph convolutional network is designed to capture multi-scale spatial dependencies and high-order interactions for classification.\n                    <jats:italic>Main results<\/jats:italic>\n                    . Experimental evaluations on a lab-acquired virtual reality-based motor imagery dataset demonstrate that the proposed framework achieves competitive three-class decoding accuracy. This performance surpasses baseline models such as EEGNet, as observed under VR-based MI conditions. In addition to enhanced decoding performance, the study enables neurophysiological interpretation by characterizing VR-induced modulations in brain network organization.\n                    <jats:italic>Significance<\/jats:italic>\n                    . VR-based AO + MI paradigm is found to enhance sensorimotor connectivity and promote large-scale network integration, indicating more coordinated neural dynamics during MI tasks. These findings suggest that the proposed framework effectively improves VR-based AO + MI decoding while providing interpretable insights into neural mechanisms, supporting its potential for advanced BCI applications.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/aeabf5","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T22:54:34Z","timestamp":1790204074000},"page":"056048","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A hierarchical graph learning framework for VR-based motor imagery decoding driven by MVMD and feature optimization"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5258-657X","authenticated-orcid":true,"given":"Kaiyue","family":"Du","sequence":"first","affiliation":[{"name":"Lanzhou Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4984-2554","authenticated-orcid":true,"given":"Wenwen","family":"Chang","sequence":"additional","affiliation":[{"name":"Lanzhou Jiaotong University"},{"name":"Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixuan","family":"Kong","sequence":"additional","affiliation":[{"name":"Lanzhou Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[{"name":"Lanzhou Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Yan","sequence":"additional","affiliation":[{"name":"Lanzhou Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Tariq","family":"Sadiq","sequence":"additional","affiliation":[{"name":"University of Essex"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"jneaeabf5bib1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s40779-025-00598-z","type":"journal-article","article-title":"Recent applications of EEG-based brain-computer-interface in the medical field, Mil","volume":"12","author":"Liu","year":"2025","journal-title":"Med. 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