The paper introduces Dual-Path Spatiotemporal Contrastive Learning (DP-STCL) for cross-subject EEG emotion recognition, using two complementary encoders—Temporal-to-Spatial and Spatial-to-Temporal—to model EEG data under different integration orders. It enforces consistency of high-level emotion representations across these paths and across subjects through three complementary relations, enhancing stability and discriminability. A LoRA-based few-shot adaptation strategy is added to handle limited labeled data from target subjects, and experiments on SEED, SEED-IV, SEED-V, and FACED datasets show DP-STCL outperforming existing methods.
By Zehui Xiao, Yi Zheng, Guixing Fu, Wenkai Lu, Lingfei He, Tao Tan, Yue Liu, Silun Wang, Junhai Xu, Mengting Liu
arXiv:2610.18587v1 Announce Type: cross
Abstract: Multimodal Emotion Recognition (MER) aims to understand human emotions by integrating information from multiple modalities such as speech and text. P...
By Ting Zhu, Huizhi Liang, Boguslaw Obara, Shufei Duan, Varun Ojha
arXiv:2606. 05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals.
By Zhihao Zhou, Weishan Ye, Li Zhang, Gan Huang, Zhen Liang
In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits of Riemannian geometry widely adopted in EEG transfer learning.
arXiv:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.
By Zheng Wang, Shuo Wang, Junhong Wang
arXiv:2607. 07773v1 Announce Type: cross Abstract: EEG-based emotion recognition is critical for mental health monitoring and affective brain-computer interfaces, yet existing deep learning approaches often treat emotion classes as isolated labels, ignoring their psychological interdependencies.
By Dongyang Kuang, Zizheng Ma, Yushan Zhang, Xiaocong Zeng