4 citations · 4 across the 8 of their papers we have counts for
8 papers
Brain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling
Weishan Ye, Yue Pan, Li Zhang +2
Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due to their high temporal comp…
EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction
Zhihao Zhou, Weishan Ye, Li Zhang +2
Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals. Unlike conventional discrete emotion rec…
Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition
Guangli Li, Canbiao Wu, Na Tian +2
Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological va…
NeuroCLIP: Brain-Inspired Prompt Tuning for EEG-to-Image Multimodal Contrastive Learning
Jiyuan Wang, Li Zhang, Xizhang Yao +7
Recent advances in brain-inspired artificial intelligence have sought to align neural signals with visual semantics using multimodal models such as CLIP. However, existing methods…
Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition
Guangli Li, Canbiao Wu, Zhehao Zhou +3
Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deployment remains limited by inter-s…
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
Jiyuan Wang, Weishan Ye, Jialin He +4
With the rapid advancement of deep learning, attention mechanisms have become indispensable in electroencephalography (EEG) signal analysis, significantly enhancing Brain-Computer…
PL-DCP: A Pairwise Learning framework with Domain and Class Prototypes for EEG emotion recognition under unseen target conditions
Guangli Li, Canbiao Wu, Zhehao Zhou +4
Electroencephalogram (EEG) signals serve as a powerful tool in affective Brain-Computer Interfaces (aBCIs) and play a crucial role in affective computing. In recent years, the intr…
M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition
Ting Luo, Jing Zhang, Yingwei Qiu +4
Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) plays a crucial role in affective computing but is limited by challenges such…