12 papers
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Jun-En Ding, Anna Zilverstand, Shihao Yang +2
The paper introduces VMoGE, a variational mixture-of-experts model that uses graph neural networks to analyze EEG connectivity across multiple frequency bands for distinguishing Al…
Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review
Shihao Yang, Xiying Huang, Danilo Bernardo +10
The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw brain signals and neural data. In t…
NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
Guoan Wang, Shihao Yang, Jun-En Ding +1
Although foundation models have demonstrated remarkable success in general domains, the application of these models to electroencephalography (EEG) analysis is constrained by subst…
NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning
Guoan Wang, Shihao Yang, Jun-en Ding +2
Electroencephalography (EEG) provides a non-invasive window into neural dynamics at high temporal resolution and plays a pivotal role in clinical neuroscience research. Despite thi…
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation
Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang +12
Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We id…
Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis
Tongze Zhang, Jun-En Ding, Melik Ozolcer +5
Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physic…