Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing

• URL: http://arxiv.org/abs/2510.22197v1
• Date: Sat, 25 Oct 2025 07:30:24 GMT
• Title: Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing
• Authors: Qingzhu Zhang, Jiani Zhong, Zongsheng Li, Xinke Shen, Quanying Liu,
• Abstract summary: Existing EEG models struggle with complex tasks like emotion recognition due to mismatches between task-specific features and broad pre-training approaches.<n>This work aims to develop a task-specific multi-dataset joint pre-training framework for cross-dataset emotion recognition.
• Score: 5.116264249622881
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Task-specific pre-training is essential when task representations diverge from generic pre-training features. Existing task-general pre-training EEG models struggle with complex tasks like emotion recognition due to mismatches between task-specific features and broad pre-training approaches. This work aims to develop a task-specific multi-dataset joint pre-training framework for cross-dataset emotion recognition, tackling problems of large inter-dataset distribution shifts, inconsistent emotion category definitions, and substantial inter-subject variability. We introduce a cross-dataset covariance alignment loss to align second-order statistical properties across datasets, enabling robust generalization without the need for extensive labels or per-subject calibration. To capture the long-term dependency and complex dynamics of EEG, we propose a hybrid encoder combining a Mamba-like linear attention channel encoder and a spatiotemporal dynamics model. Our method outperforms state-of-the-art large-scale EEG models by an average of 4.57% in AUROC for few-shot emotion recognition and 11.92% in accuracy for zero-shot generalization to a new dataset. Performance scales with the increase of datasets used in pre-training. Multi-dataset joint pre-training achieves a performance gain of 8.55% over single-dataset training. This work provides a scalable framework for task-specific pre-training and highlights its benefit in generalizable affective computing. Our code is available at https://github.com/ncclab-sustech/mdJPT_nips2025.

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