Accurate and efficient recognition of emotional states is critical for human social functioning, and impairments in this ability are associated with significant psychosocial difficulties. While electroencephalography (EEG) offers a powerful tool for objective emotion detection, existing EEG-based Emotion Recognition (EER) methods suffer from three key limitations: (1) insufficient model stability, (2) limited accuracy in processing high-dimensional nonlinear EEG signals, and (3) poor robustness against intra-subject variability and signal noise. To address these challenges, we introduce Lipschitz continuity-constrained Ensemble Learning (LEL), a novel framework that enhances EEG-based emotion recognition by enforcing Lipschitz continuity constraints on Transformer-based attention mechanisms, spectral extraction, and normalization modules. This constraint ensures model stability, reduces sensitivity to signal variability and noise, and improves generalization capability. Additionally, LEL employs a learnable ensemble fusion strategy that optimally combines decisions from multiple heterogeneous classifiers to mitigate single-model bias and variance. Extensive experiments on three public benchmark datasets (EAV, FACED, and SEED) demonstrate superior performance, achieving average recognition accuracies of 74.25%, 81.19%, and 86.79%, respectively. The official implementation codes are available at https://github.com/NZWANG/LEL.

翻译：准确高效地识别情绪状态对人类社交功能至关重要，该能力的缺损与显著的心理社会功能障碍相关。虽然脑电图（EEG）为客观情绪检测提供了有力工具，但现有的基于EEG的情绪识别方法存在三个关键局限：（1）模型稳定性不足；（2）处理高维非线性EEG信号的准确度有限；（3）对个体内变异性和信号噪声的鲁棒性较差。为应对这些挑战，我们提出了Lipschitz连续性约束的集成学习框架（LEL），该新颖框架通过对基于Transformer的注意力机制、频谱提取和归一化模块施加Lipschitz连续性约束，增强了基于EEG的情绪识别性能。该约束确保了模型稳定性，降低了对信号变异性和噪声的敏感性，并提升了泛化能力。此外，LEL采用可学习的集成融合策略，通过优化组合多个异构分类器的决策来缓解单一模型的偏差和方差问题。在三个公开基准数据集（EAV、FACED和SEED）上的大量实验表明，LEL取得了卓越性能，平均识别准确率分别达到74.25%、81.19%和86.79%。官方实现代码已发布于https://github.com/NZWANG/LEL。