3 citations · 7 across the 14 of their papers we have counts for
15 papers
BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding
Yangxuan Zhou, Yuning Chen, Chen Wu +4
Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, q…
BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding
Yangxuan Zhou, Sha Zhao, Jiquan Wang +2
Brain-Computer Interfaces (BCIs) and brain signal understanding are pivotal for clinical health and next-generation interactions. Despite this significance, its widespread adoption…
EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
Yangxuan Zhou, Sha Zhao, Jiquan Wang +2
Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that s…
Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding
Xiaoli Yang, Huiyuan Tian, Yurui Li +3
Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central q…
DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI
Jiquan Wang, Sha Zhao, Yangxuan Zhou +3
Electroencephalography (EEG) foundation models hold significant promise for universal Brain-Computer Interfaces (BCIs). However, existing approaches often rely on end-to-end fine-t…
EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models
Sha Zhao, Mingyi Peng, Haiteng Jiang +3
Scalable and generalizable analysis of brain activity is essential for advancing both clinical diagnostics and cognitive research. Electroencephalography (EEG), a non-invasive moda…
EMOD: A Unified EEG Emotion Representation Framework Leveraging V-A Guided Contrastive Learning
Yuning Chen, Sha Zhao, Shijian Li +1
Emotion recognition from EEG signals is essential for affective computing and has been widely explored using deep learning. While recent deep learning approaches have achieved stro…
SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
Yangxuan Zhou, Sha Zhao, Jiquan Wang +4
Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that inte…
CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning
Yurui Li, Yuxuan Chen, Xiaoli Yang +2
The critical role of division of labor (DOL) in enhancing cooperation is well-recognized in real-world applications. Consequently, many cooperative multi-agent reinforcement learni…
SpectralKD: A Unified Framework for Interpreting and Distilling Vision Transformers via Spectral Analysis
Huiyuan Tian, Bonan Xu, Shijian Li +1
Knowledge Distillation (KD) has achieved widespread success in compressing large Vision Transformers (ViTs), but a unified theoretical framework for both ViTs and KD is still lacki…
Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation
Yangxuan Zhou, Sha Zhao, Jiquan Wang +5
Sleep staging is crucial for assessing sleep quality and diagnosing related disorders. Recent deep learning models for automatic sleep staging using polysomnography often suffer fr…
A Framework For Image Synthesis Using Supervised Contrastive Learning
Yibin Liu, Jianyu Zhang, Li Zhang +2
Text-to-image (T2I) generation aims at producing realistic images corresponding to text descriptions. Generative Adversarial Network (GAN) has proven to be successful in this task.…
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
Jiquan Wang, Sha Zhao, Zhiling Luo +5
Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding…
Generalizable Sleep Staging via Multi-Level Domain Alignment
Jiquan Wang, Sha Zhao, Haiteng Jiang +3
Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other un…
Multi-Depth Branch Network for Efficient Image Super-Resolution
Huiyuan Tian, Li Zhang, Shijian Li +2
A longstanding challenge in Super-Resolution (SR) is how to efficiently enhance high-frequency details in Low-Resolution (LR) images while maintaining semantic coherence. This is p…