用Transformer捕捉脑电情绪信号的时空特征，准确率更高且更稳定。
Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition
- 分三模块：去噪预处理、局部注意力学区域特征、模糊注意力建模时序同步
- 在三个数据集上分类准确率超现有方法，信号长度变化时性能波动小
- 适合做跨被试情绪识别，计算量低且泛化性强
脑电图（EEG）因其高时间分辨率和低成本，是监测脑活动的常用手段。为提升复杂脑电信号的分析能力，本文提出EEG-TransNet，用于捕捉脑电信号的时间、空间与同步特征。该模型包含三个核心模块：1）基于ResNet与小波去噪的预处理与特征提取模块；2）局部自注意力块，用于学习区域特征；3）模糊注意力同步变换器（FAST），建模时空依赖关系。在三个脑电数据集（BETA、SEED、DepEEG）上的大量实验表明，该模型在分类准确率和对不同信号长度的鲁棒性方面均优于现有方法。消融实验证实局部自注意力块对性能提升有贡献，且解码器中引入深度可分离卷积，在保持高精度的同时降低了计算复杂度。模型在跨被试场景下表现稳定，性能差异小，展现出作为脑电情绪识别工具的强泛化潜力。
原文摘要 · Abstract (English)
Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness. To enhance the analysis of complex EEG data, we propose EEG-TransNet, an architecture designed to capture temporal, regional, and synchronous features of EEG signals. EEG-TransNet introduces three key modules: 1) a preprocessing and feature extraction module leveraging ResNet and wavelet-based denoising, 2) a Local Self-Attention Block for regional feature learning, and 3) a Fuzzy-Attention Synchronous Transformer (FAST) to model spatiotemporal dependencies. Through extensive experiments on three EEG datasets (BETA, SEED, and DepEEG), the proposed model consistently outperforms other methods in terms of classification accuracy and robustness across varying signal lengths. Ablation studies confirm the contribution of the Local Self-Attention Block in improving performance, and the inclusion of depthwise separable convolutions in the decoder reduces computational complexity while maintaining high accuracy. EEG-TransNet's ability to generalize across subjects with minimal performance variation highlights its potential as a robust tool for EEG-based brain activity classification and emotion recognition tasks.
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