Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition

• URL: http://arxiv.org/abs/2507.14698v1
• Date: Sat, 19 Jul 2025 17:23:38 GMT
• Title: Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition
• Authors: Xuetao Lin, Tianhao Peng, Peihong Dai, Yu Liang, Wenjun Wu,
• Abstract summary: SST-CL is a novel framework integrating spatial-temporal transformers with curriculum learning.<n>An intensity-aware curriculum learning strategy guides training from high-intensity to low-intensity emotional states.<n>Experiments on three benchmark datasets demonstrate state-of-the-art performance across various emotional intensity levels.
• Score: 2.847161275680418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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