EmotionMIL: An End-to-End Multiple Instance Learning Framework for Emotion Recognition From EEG Signals

作者：Jun Xiao, Feifei Qi, Wang Li, Yanbin He, Jin-Gang Yu, Wei Wu, Zhuliang Yu, Yuanqing Li, Zhenghui Gu, Tianyou Yu · 发表于：IEEE Transactions on Affective Computing · 年份：2025 · DOI：10.1109/taffc.2025.3581388 · 被引用次数：3 · 研究领域：Emotion and Mood Recognition、EEG and Brain-Computer Interfaces

Emotion recognition from EEG signals offers significant advantages in affective computing, as EEG more accurately reflects internal emotional states than other modalities, such as facial expressions or peripheral physiological signals. Modeling and capturing subtle affective changes over time is crucial for real-world applications to achieve better human-computer interaction. However, training such models usually requires segment-level emotion labels, which are costly and may not be feasible. Assigning the overall label to all EEG segments within a trial can lead to inaccurate model training and degraded performance, as emotions evolve continuously. This highlights the need for models capable of learning from trial-wise emotion labels while capturing temporal dynamics of emotional responses within each segment because trial-wise post-stimulus labels are more accessible. To this end, we propose EmotionMIL, an end-to-end EEG-based emotion recognition framework that leverages recent advances in deep multiple instance learning (MIL). This framework enables robust emotion recognition from weakly labeled EEG signals and identifies the most prominent emotional responses. EmotionMIL captures the temporal dynamics of emotions using a retentive self-attention mechanism, which adaptively assigns weights to EEG segments based on their relevance in predicting the overall emotion label. A pseudo-bag augmentation strategy is also introduced to enhance the model's generalization ability by g...