用多尺度反向Mamba模型，仅四通道脑电就能高精度识别情绪。
miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models
- 设计多尺度时序块与时空融合块，联合建模脑电信号的局部细节与全局动态。
- 在三大数据集上分别达到94.86%、94.94%、91.36%准确率，优于现有方法。
- 无需特定时频特征提取，适合资源受限的情绪计算场景。
基于脑电的情绪识别在脑机接口领域具有重要潜力，关键挑战在于从脑电信号中提取具有区分性的时空特征。现有研究通常依赖领域特定的时频特征，并分开分析时间依赖性和空间特性，忽略了局部-全局关系与时空动态之间的交互。为此，我们提出一种新型网络——多尺度反向Mamba（MS-iMamba），包含多尺度时序块（MSTB）和时空融合块（TSFB）。MSTB用于捕捉不同尺度子序列上的局部细节与全局时间依赖性；TSFB采用反向Mamba结构，聚焦动态时间依赖性与空间特征之间的交互。MS-iMamba的主要优势在于利用重构的多尺度脑电信号，无需进行特定时频特征提取，即可挖掘时空特征间的交互作用。在DEAP、DREAMER和SEED数据集上的实验结果表明，该模型仅使用四通道脑电信号，分类准确率分别达到94.86%、94.94%和91.36%，显著优于当前最优方法。
原文摘要 · Abstract (English)
EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge lies in 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 interaction between local-global relationships and spatiotemporal dynamics. To address this, we propose a novel network called Multi-Scale Inverted Mamba (MS-iMamba), 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 MS-iMamba lies in its ability to leverage reconstructed multi-scale EEG sequences, exploiting the interaction between temporal and spatial features without the need for domain-specific time-frequency feature extraction. Experimental results on the DEAP, DREAMER, and SEED datasets demonstrate that MS-iMamba achieves classification accuracies of 94.86%, 94.94%, and 91.36%, respectively, using only four-channel EEG signals, outperforming state-of-the-art methods.
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