Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913997335298048 |
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| author | Lin, Xuetao Peng, Tianhao Dai, Peihong Liang, Yu Wu, Wenjun |
| author_facet | Lin, Xuetao Peng, Tianhao Dai, Peihong Liang, Yu Wu, Wenjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition Lin, Xuetao Peng, Tianhao Dai, Peihong Liang, Yu Wu, Wenjun Machine Learning Artificial Intelligence Human-Computer Interaction Signal Processing 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. |
| title | Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2507.14698 |