Dual path method improves cross subject emotion recognition from EEG
Beyond Modeling Order: Dual-Path Spatiotemporal Representation Learning for Cross-Subject EEG Emotion Recognition
Machine Learning
Summary
Recognizing emotions from brain signals is hard because people's brains work differently. The authors suggest using two different ways to look at brain data—first focusing on how signals change over time, then across space, and vice versa. By making sure these two views agree when identifying emotions, their method learns better, more stable emotion patterns that work across different people. They also created a way to adapt the system quickly with only a little new data. Tests showed their approach works better than older ones.
What this means in practice
- •For brain computer interface developers: Improve emotion recognition models to generalize better across new users with minimal data by using dual-path contrastive learning.
- •For affective computing engineers: Build more robust EEG emotion detection systems by integrating dual-order spatiotemporal modeling to reduce subject variability effects.
Authors
Zehui Xiao, Yi Zheng, Guixing Fu, Wenkai Lu, Lingfei He, Tao Tan, Yue Liu, Silun Wang, Junhai Xu, Mengting Liu
Abstract
Cross-subject generalization remains a major challenge for EEG-based emotion recognition due to pronounced inter-subject variability in neural activity and emotion-related spatiotemporal patterns. Existing methods mainly alleviate inter subject differences through distribution alignment or contrastive learning, while paying limited attention to representation bias associated with fixed spatiotemporal modeling orders. We hypothesize that high-level emotion representations of the same EEG signal should remain consistent across different spatiotemporal modeling paths. Based on this hypothesis, we propose a Dual-Path Spatiotemporal Contrastive Learning (DP-STCL) framework for cross-subject EEG emotion recognition. DP-STCL employs two complementary encoders with independent parameters, namely a Temporal-to-Spatial (TS) path and a Spatial to-Temporal (ST) path, to model EEG spatiotemporal information under different integration orders. During representation learning, the same emotional stimulus is used to establish cross subject associations, while three complementary relations jointly constrain representation variations across subjects and modeling paths. This promotes stable and discriminative stimulus-related emotion representations. To further handle limited labeled target subject data, we introduce a LoRA-based few-shot adaptation strategy with low parameter overhead. Extensive cross-subject experiments on SEED, SEED-IV, SEED-V, and FACED show that DP-STCL consistently outperforms the compared methods, demonstrating its effectiveness in improving cross-subject EEG emotion recognition.