用Mamba架构高效捕捉脑电情绪的多尺度时空动态
MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
- 分多时窗处理信号，结合全局局部脑图结构建模
- 单层MSST-Mamba在3个数据集上超越主流方法
- 适合实时情绪识别，推理快至毫秒级
基于脑电的情绪识别面临捕捉多尺度时空动态与保证实时计算效率的双重挑战。现有方法常简化时间粒度与空间层次，影响精度。为此，我们提出多尺度时空图Mamba（MSGM），融合多窗口时间分割、双模态空间图建模及Mamba架构的高效融合机制。通过在不同时间尺度上分割脑电信号，并构建含神经解剖先验的全局-局部图，MSGM有效捕捉细微情绪波动与脑区层级连接。结合多深度图卷积网络与令牌嵌入融合模块，配合Mamba的状态空间建模，实现线性复杂度下的动态时空交互。值得注意的是，仅使用一层MSST-Mamba，MSGM在SEED、THU-EP和FACED数据集上均超越现有先进方法，在跨被试情绪分类中表现优异，同时在NVIDIA Jetson Xavier NX上实现毫秒级推理，具备强鲁棒性。
原文摘要 · Abstract (English)
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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