7 papers
BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding
Yangxuan Zhou, Sha Zhao, Yuning Chen +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…
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…