Dynamic graph based attention spectral network for motor imagery-brain computer interface.

Overview

1. The School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
2. The Institute for Super Robotics (Huangpu), Guangzhou, China
3. The Pazhou Laboratory, Guangzhou, China
4. The School of Architecture, South China University of Technology, Guangzhou, China
5. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China

Abstract

Motor imagery-based brain computer interface (MI-BCI) have been increasingly adopted in neurorehabilitation and related fields. The performance of MI-electroencephalogram (MI-EEG) decoding algorithms is central to the advancement of MI-BCI. However, current studies often lack rigorous investigation into the brain's complex network organization. Moreover, most existing methods do not incorporate the cross-frequency coupling (CFC) phenomena that occur during MI into their algorithmic designs, nor do they adequately account for how temporal dynamics across different MI stages influence decoding outcomes. To address these limitations, we propose the Dynamic Spectral-Spatial Interaction Convolution Neural Network (DSSICNN), a parameter-efficient MI-EEG decoding framework that jointly extracts temporal-spectral-spatial features. DSSICNN adopts a dual-branch parallel architecture to concurrently learn spatial representations in both Euclidean and non-Euclidean domains. It further integrates a CFC-inspired attention module to model cross-spectral interactions, followed by an additional attention mechanism that quantifies the contributions of distinct MI stages to decoding performance. DSSICNN achieves decoding performance on two public datasets that surpasses the current state-of-the-art (SOTA) under both session-dependent and session-independent settings. Beyond its empirical advantages, DSSICNN offers design insights for developing Graph Neural Network (GNN)-based MI-EEG decoding algorithms and provides a network neuroscience-inspired perspective for understanding the neurophysiological mechanisms underlying MI.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

• bbci.de/competition/iv, at bbci.de; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.bbci.de/competition/iv/#dataset2a; https://gigadb.org/dataset/100542.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 42 references.

Cite

This paper

Shao, Z., Gu, Z., Che, L., Yu, Z., & Li, Y. (2026). Dynamic graph based attention spectral network for motor imagery-brain computer interface. Frontiers in human neuroscience, 20, 1755549. https://doi.org/10.3389/fnhum.2026.1755549

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