miMamba: EEG-Based Emotion Recognition With Multi-Scale Inverted Mamba Models

EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge is extracting discriminative spatiotemporal features from electroencephalogram (EEG) signals. Existing studies often rely on domain-specific time-frequency features and analyze temporal dependencies and spatial characteristics separately, neglecting the local-global relationships and the interaction in spatiotemporal dynamics. To address this, we propose a novel network called Multi-scale Inverted Mamba (miMamba), which consists of Multi-Scale Temporal Blocks (MSTB) and Temporal-Spatial Fusion Blocks (TSFB). Specifically, MSTBs are designed to capture both local details and global temporal dependencies across different scale subsequences. The TSFBs, implemented with an inverted Mamba structure, focus on the interaction between dynamic temporal dependencies and spatial characteristics. The primary advantage of miMamba lies in its ability to leverage transformed multi-scale EEG sequences, exploiting the interaction between temporal and spatial features without the need for domain-specific time-frequency feature extraction. Experiments show that using only four EEG channels, miMamba achieves remarkable average recognition accuracies for Valence and Arousal classification: 94.86% on the DEAP dataset, 94.94% on the DREAMER dataset, and 91.36% on the SEED dataset. These results underscore the model's superior performance in multidimensional emotion recognition tasks and its potential for practical applications in resource-constrained affective computing scenarios.