EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition

TL;DR

This paper introduces a novel self-supervised framework called Soft Contrastive Masked Modeling (SCMM) that significantly improves cross-corpus EEG-based emotion recognition by capturing emotion dynamics and enhancing feature transferability.

Contribution

SCMM integrates soft contrastive learning with hybrid masking to better model emotional continuity and improve cross-corpus generalization in EEG-based emotion recognition.

Findings

Achieves state-of-the-art accuracy on SEED, SEED-IV, and DEAP datasets.

Outperforms previous methods by an average of 4.26% in cross-corpus settings.

Demonstrates robust transferability of emotion representations across datasets.

Abstract

Emotion recognition using electroencephalography (EEG) signals has attracted increasing attention in recent years. However, existing methods often lack generalization in cross-corpus settings, where a model trained on one dataset is directly applied to another without retraining, due to differences in data distribution and recording conditions. To tackle the challenge of cross-corpus EEG-based emotion recognition, we propose a novel framework termed Soft Contrastive Masked Modeling (SCMM). Grounded in the theory of emotional continuity, SCMM integrates soft contrastive learning with a hybrid masking strategy to effectively capture emotion dynamics (refer to short-term continuity). Specifically, in the self-supervised learning stage, we propose a soft weighting mechanism that assigns similarity scores to sample pairs, enabling fine-grained modeling of emotional transitions and capturing the temporal continuity of human emotions. To further enhance representation learning, we design a similarity-aware aggregator that fuses complementary information from semantically related samples based on pairwise similarities, thereby improving feature expressiveness and reconstruction quality. This dual design contributes to a more discriminative and transferable representation, which is crucial for robust cross-corpus generalization. Extensive experiments on the SEED, SEED-IV, and DEAP datasets show that SCMM achieves state-of-the-art (SOTA) performance, outperforming the second-best method by an average accuracy of 4.26% under both same-class and different-class cross-corpus settings. The source code is available at https://github.com/Kyler-RL/SCMM.