Special Seminar by Prof Chun-Chuan Chen

Title: Digital Health and NeuroEngineering: from Bench to Bedside

Abstract: Advances in digital health and neuroengineering are rapidly reshaping the landscape of modern medicine, creating new pathways to understand, diagnose, and treat disorders. This talk, Digital Health and NeuroEngineering: From Bench to Bedside, explores the convergence of neuroscience, engineering, and artificial intelligence (AI) in transforming care for neuropsychiatric and neurodevelopmental conditions. Focusing on three key areas- Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and substance use disorders (SUDs)- I will illustrate how emerging technologies are translating basic neuroscience into clinically actionable tools. Through the use of multimodal wearable sensors, virtual reality (VR), and AI-driven analytics, we can move beyond the limitations of traditional clinical observation to develop novel metrics for diagnosis and treatment monitoring. These approaches offer the potential for timely, personalised interventions, effectively bridging the gap between laboratory research and real-world clinical application.

Bio: Professor Chun-Chuan Chen obtained her PhD in Computational Neuroscience from National Yang Ming University in 2009, with research training at the Welcome Trust Centre for Neuroimaging, University College London. She has held academic positions at several major Taiwanese institutions and is currently a Professor in the Department of Biomedical Sciences and Engineering at National Central University, Taiwan. Her work spans neural engineering, precision medicine, and the application of AI and virtual reality in neuroimaging and brain-computer interface research. Professor Chen is internationally recognised for her contributions to M/EEG signal processing and the development of novel VR-based neurocognitive assessment tools, particularly for disorders such as ADHD, autism, and substance use disorders. Her recent publications focus on integrating neurophysiological data with machine learning to improve clinical screening and rehabilitation strategies.