68 citations · 88 across the 11 of their papers we have counts for
12 papers
AutoBCI: Forecast-Guided Agentic Neural Architecture Discovery for EEG-Based Brain--Computer Interfaces
Muyun Jiang, Yi Ding, Wei Zhang +9
EEG-based brain-computer interfaces support a broad range of applications, yet designing decoding architectures that perform well across diverse tasks remains challenging. We intro…
EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding
Shuailei Zhang, Muyun Jiang, Wei Zhang +5
Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We p…
EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
Runhe Zhou, Shanglin Li, Guanxiang Huang +5
Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical potential…
Decoupled Hierarchical Distillation for Multimodal Emotion Recognition
Yong Li, Yuanzhi Wang, Yi Ding +3
Human multimodal emotion recognition (MER) seeks to infer human emotions by integrating information from language, visual, and acoustic modalities. Although existing MER approaches…
LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces
Muyun Jiang, Shuailei Zhang, Zhenjie Yang +9
Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interf…
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer
Muyun Jiang, Yi Ding, Wei Zhang +14
Covert speech involves imagining speaking without audible sound or any movements. Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understandi…
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities
Wei-Bang Jiang, Xi Fu, Yi Ding +1
Multimodal physiological signals, such as EEG, ECG, EOG, and EMG, are crucial for healthcare and brain-computer interfaces. While existing methods rely on specialized architectures…
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection
Yong Li, Menglin Liu, Zhen Cui +5
Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and…
Beyond Overfitting: Doubly Adaptive Dropout for Generalizable AU Detection
Yong Li, Yi Ren, Xuesong Niu +3
Facial Action Units (AUs) are essential for conveying psychological states and emotional expressions. While automatic AU detection systems leveraging deep learning have progressed,…
Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers
Yi Ding, Joon Hei Lee, Shuailei Zhang +2
Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a…
EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
Yi Ding, Chengxuan Tong, Shuailei Zhang +4
Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial a…
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
Yi Ding, Yong Li, Hao Sun +5
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs).…