Robust EEG-based Emotion Recognition from Coarse Trial-level Labels under Intra-trial Affective Variability

Xiaocong Zeng, Buxuan Wang, Xin Yang, Pei Huang, Dongyang Kuang

EEG-based emotion recognition has become an active research area in brain-computer interfaces. However, fine-grained affective annotation is costly, so most datasets provide only a global label for each long EEG trial. For practical emotion recognition, trials are commonly divided into short temporal windows, with the global label propagated to all windows for training. This practice treats trial-level emotion as temporally uniform, although affective responses may fluctuate within a trial. Consequently, windows that weakly reflect or deviate from the global emotional state may receive unreliable labels, posing a key challenge to robust EEG-based emotion recognition. To address this challenge, we propose VARY, a variability-aware robust learning framework for EEG-based emotion recognition from coarse trial-level labels. VARY incorporates a Two-stage Sample Selection (TSS) strategy to identify EEG windows with reliable emotional labels, and introduces MixAug, a data augmentation method tailored for EEG to improve model robustness under limited and unreliable supervision. We comprehensively evaluate VARY under the setting of label-noise learning on two public datasets, using three…