用课程学习提升脑电情绪识别，兼顾空间时间特征与强度变化
Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition
- 设计时空变换器，同时捕捉脑电信号的空间关联与多尺度时序动态
- 在三个数据集上达到顶尖性能，尤其在低强度情绪识别中提升显著
- 适合需要适应真实情绪波动场景的脑机接口研究者
基于脑电的情绪识别对发展自适应脑机通信系统至关重要，但实际应用面临两大挑战：（1）有效整合非平稳的空间-时间神经模式；（2）在真实场景中对动态情绪强度变化的鲁棒适应。本文提出SST-CL框架，融合时空变换器与课程学习。方法引入两个核心组件：空间编码器建模通道间关系，时间编码器通过窗口注意力机制捕获多尺度依赖，实现对脑电信号空间相关性与时间动态的同步提取。配套采用强度感知的课程学习策略，基于双难度评估动态调度样本，逐步从高情绪强度向低强度状态引导训练。在三个基准数据集上的全面实验表明，该方法在不同情绪强度水平下均达到最先进性能，消融实验证实架构组件与课程学习机制均不可或缺。
原文摘要 · Abstract (English)
EEG-based emotion recognition plays an important role in developing adaptive brain-computer communication systems, yet faces two fundamental challenges in practical implementations: (1) effective integration of non-stationary spatial-temporal neural patterns, (2) robust adaptation to dynamic emotional intensity variations in real-world scenarios. This paper proposes SST-CL, a novel framework integrating spatial-temporal transformers with curriculum learning. Our method introduces two core components: a spatial encoder that models inter-channel relationships and a temporal encoder that captures multi-scale dependencies through windowed attention mechanisms, enabling simultaneous extraction of spatial correlations and temporal dynamics from EEG signals. Complementing this architecture, an intensity-aware curriculum learning strategy progressively guides training from high-intensity to low-intensity emotional states through dynamic sample scheduling based on a dual difficulty assessment. Comprehensive experiments on three benchmark datasets demonstrate state-of-the-art performance across various emotional intensity levels, with ablation studies confirming the necessity of both architectural components and the curriculum learning mechanism.
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