miMamba：基于多尺度倒置曼巴模型的脑电情感识别
miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models
AI总结：
研究脑电情感识别，提出多尺度倒置曼巴（MS-iMamba）网络，含多尺度时间块和时空融合块，能捕捉不同尺度子序列特征及时空交互，实验表明其仅用四通道脑电信号就优于现有方法。
摘要：
基于脑电图（EEG）的情感识别在脑机接口领域具有巨大潜力。关键挑战在于从脑电信号中提取有区分力的时空特征。现有研究常依赖特定领域的时频特征，分别分析时间依赖性和空间特征，忽视局部-全局关系和时空动态的相互作用。为解决此问题，我们提出了一种名为多尺度倒置曼巴（MS-iMamba）的新型网络，它由多尺度时间块（MSTB）和时空融合块（TSFB）组成。具体而言，MSTB旨在捕捉不同尺度子序列的局部细节和全局时间依赖性。TSFB采用倒置曼巴结构，专注于动态时间依赖性和空间特征之间的相互作用。MS-iMamba的主要优势在于它能够利用重建的多尺度脑电序列，利用时间和空间特征之间的相互作用，而无需特定领域的时频特征提取。在DEAP、DREAMER和SEED数据集上的实验结果表明，MS-iMamba仅使用四通道脑电信号就能分别达到94.86%、94.94%和91.36%的分类准确率，优于现有方法。
英文摘要：
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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