MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition

• URL: http://arxiv.org/abs/2507.15914v1
• Date: Mon, 21 Jul 2025 17:18:00 GMT
• Title: MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
• Authors: Hanwen Liu, Yifeng Gong, Zuwei Yan, Zeheng Zhuang, Jiaxuan Lu,
• Abstract summary: We propose a novel framework integrating multi-window temporal segmentation, bi-temporal graph modeling, and efficient fusion via the Mamba architecture.<n>By segmenting EEG signals across diverse temporal scales, MSGM effectively captures fine-grained emotional fluctuations and hierarchical brain connectivity.<n> MSGM surpasses leading methods in the field on the SEED, T-HU-EP, and FACEDEP. Notably, with just one MSST-Mamba layer modeling, MSGM surpasses leading methods in the field on the SEED, T-HU-EP, and FACEDEP.
• Score: 2.9197024670810867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EEG-based emotion recognition struggles with capturing multi-scale spatiotemporal dynamics and ensuring computational efficiency for real-time applications. Existing methods often oversimplify temporal granularity and spatial hierarchies, limiting accuracy. To overcome these challenges, we propose the Multi-Scale Spatiotemporal Graph Mamba (MSGM), a novel framework integrating multi-window temporal segmentation, bimodal spatial graph modeling, and efficient fusion via the Mamba architecture. By segmenting EEG signals across diverse temporal scales and constructing global-local graphs with neuroanatomical priors, MSGM effectively captures fine-grained emotional fluctuations and hierarchical brain connectivity. A multi-depth Graph Convolutional Network (GCN) and token embedding fusion module, paired with Mamba's state-space modeling, enable dynamic spatiotemporal interaction at linear complexity. Notably, with just one MSST-Mamba layer, MSGM surpasses leading methods in the field on the SEED, THU-EP, and FACED datasets, outperforming baselines in subject-independent emotion classification while achieving robust accuracy and millisecond-level inference on the NVIDIA Jetson Xavier NX.

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