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 signals. Existing studies often rely on domain-specific time-frequency features and analyze temporal dependencies and spatial characteristics separately%2C neglecting the interaction between local-global relationships and spatiotemporal dynamics. To address this%2C we propose a novel network called Multi-Scale Inverted Mamba (MS-iMamba)%2C which consists of Multi-Scale Temporal Blocks (MSTB) and Temporal-Spatial Fusion Blocks (TSFB). Specifically%2C MSTBs are designed to capture both local details and global temporal dependencies across different scale subsequences. The TSFBs%2C implemented with an inverted Mamba structure%2C focus on the interaction between dynamic temporal dependencies and spatial characteristics. The primary advantage of MS-iMamba lies in its ability to leverage reconstructed multi-scale EEG sequences%2C exploiting the interaction between temporal and spatial features without the need for domain-specific time-frequency feature extraction. Experimental results on the DEAP%2C DREAMER%2C and SEED datasets demonstrate that MS-iMamba achieves classification accuracies of 94.86%25%2C 94.94%25%2C and 91.36%25%2C respectively%2C using only four-channel EEG signals%2C outperforming state-of-the-art methods.?quality=80&w=800)
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