68 citations · 83 across the 16 of their papers we have counts for
19 papers
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models
Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8
EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pret…
ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models
Yubo Wang, Jingying Ma, Xinliang Zhou +8
EEG foundation models (EFMs) capture reusable knowledge from large-scale EEG data, while many EEG recordings also include companion physiological signals that provide complementary…
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…
Hierarchical Vision-Language Interaction for Facial Action Unit Detection
Yong Li, Yi Ren, Yizhe Zhang +5
Facial Action Unit (AU) detection seeks to recognize subtle facial muscle activations as defined by the Facial Action Coding System (FACS). A primary challenge w.r.t AU detection i…
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…
EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training
Yuting Tang, Weibang Jiang, Shanglin Li +5
Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable…
DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection
Yuxin Li, Xiangyu Zhang, Yifei Li +4
Speech is a scalable and non-invasive biomarker for early mental health screening. However, widely used depression datasets like DAIC-WOZ exhibit strong coupling between linguistic…
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…
BrainPro: Process-conditioned Pretraining with Topographic Retrieval for Large EEG Models
Yi Ding, Muyun Jiang, Weibang Jiang +6
EEG foundation models are pretrained on heterogeneous corpora, but two challenges remain underexplored: (1) experimental protocols provide mental-process cues that are rarely used…
EEG-to-Gait Decoding via Phase-Aware Representation Learning
Xi Fu, Weibang Jiang, Rui Liu +2
Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This stud…
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…
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding
Xi Fu, Rui Liu, Aung Aung Phyo Wai +4
Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes, the need for accurate temporal and spectral feat…
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…
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).…