MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
This work addresses the challenge of capturing multi-scale spatiotemporal dynamics in EEG emotion recognition for real-time applications, representing a novel method for a known bottleneck.
The paper tackled the problem of EEG-based emotion recognition by proposing the Multi-Scale Spatiotemporal Graph Mamba (MSGM) framework, which achieved state-of-the-art performance on SEED, THU-EP, and FACED datasets with robust accuracy and millisecond-level inference on the NVIDIA Jetson Xavier NX.
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.