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

--EEG-based emotion recognition struggles with capturing multi-scale spatiotemporal dynamics and ensuring computational efficiency for real-time applications. T o 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. 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. MOTION recognition has emerged as a critical research frontier with far-reaching implications for human-computer interaction, mental health monitoring, and neurosci-entific exploration [1] [2] [3]. The ability to decode emotional states in real-time promises to revolutionize intelligent systems by enhancing user adaptability and bolstering clinical applications through early detection and management of emotional disorders [4] [5]. As these capabilities become increasingly vital in healthcare and artificial intelligence, there is an urgent need for robust, efficient, and neurophysiologically grounded approaches to overcome both theoretical complexities and practical deployment challenges [6]. Electroencephalography (EEG) stands out as a premier modality for emotion recognition, owing to its unparalleled capacity to non-invasively record brain activity with high temporal resolution, directly capturing the neural signatures of emotional processes [7]. Hanwen Liu and Yifeng Gong are with the School of Electronics and Communication Engineering, Sun Y at-sen University, Shenzhen, 518107, China, e-mail: (liuhw56, gongyf9)@mail2.sysu.edu.cn. Zuwei Y an is with the College of Communication Engineering, Jilin University, Changchun, 130012, China, e-mail: yanzw2422@mails.jlu.edu.cn.