PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.
Overview
- School of Integrated Circuits, Shandong University, Jinan 250199, China
- Department of Biomedical Engineering, New York University, New York, NY 10012, USA
- Shenzhen Research Institute of Shandong University, Shenzhen 518000, China
- Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology, Ministry of Education, Dalian 116024, China
Abstract
Motor imagery brain–computer interfaces (MI-BCIs) have important applications in neurorehabilitation, assistive communication, and non-muscular human–machine interaction. 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’s 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.
Reproduced under the paper's license (CC BY), from the paper cited above.
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Data
Datasets cited
- bbci.de/
competition/ , at bbci.de; found in “Data Availability Statement”iv
Data Availability Statement
A publicly available dataset was analyzed in this study. This data can be found here: BCI Competition IV 2a dataset http://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 6 funders, 61 references.
Cite
This paper
Yuan, J., Zhang, R., Zhao, Y., Zhou, W., Tian, L., & Liu, G. (2026). PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification. Biomimetics (Basel, Switzerland), 11(6), 377. https://
BibTeX
@article{yuan2026pg,
author = {Yuan, Jiahui and Zhang, Rui and Zhao, Yazhou and Zhou, Weidong and Tian, Lan and Liu, Guoyang},
title = {{PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification}},
journal = {Biomimetics (Basel, Switzerland)},
year = {2026},
month = may,
volume = {11},
number = {6},
pages = {377},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2313-7673},
doi = {10.3390/
url = {https://
pmid = {42345666},
pmcid = {PMC13297571}
}
RIS
TY - JOUR
AU - Yuan, Jiahui
AU - Zhang, Rui
AU - Zhao, Yazhou
AU - Zhou, Weidong
AU - Tian, Lan
AU - Liu, Guoyang
TI - PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification
T2 - Biomimetics (Basel, Switzerland)
J2 - Biomimetics (Basel)
PY - 2026
DA - 2026/
VL - 11
IS - 6
SP - 377
SN - 2313-7673
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
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