Fusion of Multiscale Features Via Centralized Sparse-attention Network for EEG Decoding

• URL: http://arxiv.org/abs/2512.18689v2
• Date: Tue, 23 Dec 2025 14:46:41 GMT
• Title: Fusion of Multiscale Features Via Centralized Sparse-attention Network for EEG Decoding
• Authors: Xiangrui Cai, Shaocheng Ma, Lei Cao, Jie Li, Tianyu Liu, Yilin Dong,
• Abstract summary: We propose a Fusion of Multiscale Features via Sparse-attention Network (EEG-CSANet), a centralized sparse-attention network.<n>EEG-CSANet achieves robustness and adaptability across various EEG decoding tasks.<n>In the future, we hope that EEG-CSANet could serve as a promising baseline model in the field of EEG signal decoding.
• Score: 16.451536844084483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalography (EEG) signal decoding is a key technology that translates brain activity into executable commands, laying the foundation for direct brain-machine interfacing and intelligent interaction. To address the inherent spatiotemporal heterogeneity of EEG signals, this paper proposes a multi-branch parallel architecture, where each temporal scale is equipped with an independent spatial feature extraction module. To further enhance multi-branch feature fusion, we propose a Fusion of Multiscale Features via Centralized Sparse-attention Network (EEG-CSANet), a centralized sparse-attention network. It employs a main-auxiliary branch architecture, where the main branch models core spatiotemporal patterns via multiscale self-attention, and the auxiliary branch facilitates efficient local interactions through sparse cross-attention. Experimental results show that EEG-CSANet achieves state-of-the-art (SOTA) performance across five public datasets (BCIC-IV-2A, BCIC-IV-2B, HGD, SEED, and SEED-VIG), with accuracies of 88.54%, 91.09%, 99.43%, 96.03%, and 90.56%, respectively. Such performance demonstrates its strong adaptability and robustness across various EEG decoding tasks. Moreover, extensive ablation studies are conducted to enhance the interpretability of EEG-CSANet. In the future, we hope that EEG-CSANet could serve as a promising baseline model in the field of EEG signal decoding. The source code is publicly available at: https://github.com/Xiangrui-Cai/EEG-CSANet

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