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From a bionic neural-interfacing perspective, MI-BCI decoding provides a computational bridge between biological motor intention and external machine control. However, reliable motor imagery electroencephalography (MI-EEG) classification remains challenging due to the highly non-stationary features of MI-EEG and limited interpretability. In this work, we propose PG-MCTFormer, a prior-guided multi-scale convolutional Transformer for MI-EEG classification that integrates rhythm-aware temporal filtering, dual-scale spatial modeling, and contextual decoding within a unified architecture. We evaluated the model on the publicly available BCI Competition IV 2a dataset, achieving 85.08% average accuracy and a Cohen\u2019s kappa of 0.80, with significant performance improvement over the traditional methods. Comprehensive multi-view interpretability analyses in the frequency, temporal, and spatial domains further show that the learned filters remain aligned with canonical MI-related bands, discriminative evidence concentrates in the middle-to-late imagery interval, and the spatial prior is refined into subject-adaptive sensorimotor topographic patterns. These results indicate that explicit neurophysiological priors can improve both the robustness and the interpretability of MI-EEG decoders for biomimetic neural-interface applications.<\/jats:p>","DOI":"10.3390\/biomimetics11060377","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:11:01Z","timestamp":1780333861000},"page":"377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Jiahui","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8342-4328","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yazhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, New York University, New York, NY 10012, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-1696","authenticated-orcid":false,"given":"Weidong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250199, China"},{"name":"Shenzhen Research Institute of Shandong University, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5879-809X","authenticated-orcid":false,"given":"Guoyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250199, China"},{"name":"Shenzhen Research Institute of Shandong University, Shenzhen 518000, China"},{"name":"Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology, Ministry of Education, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1145\/1941487.1941506","article-title":"Brain-Computer Interfaces for Communication and Control","volume":"54","author":"McFarland","year":"2011","journal-title":"Commun. 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