Intelligent neurotechnology is rapidly moving beyond the lab toward systems that can sense, interpret, and modulate brain activity in everyday settings. This talk highlights methods and applications from our Neuro-AI Lab that enable that transition. I will first introduce noninvasive brain–computer interfaces that decode cognitive and affective states from EEG using deep learning, including domain-alignment strategies that shorten calibration and support wearable, low-density devices. I will then present closed-loop, personalized neuromodulation for major depressive disorder, where real-time EEG phase tracking triggers adaptive iTBS to improve efficacy and tolerability. For diagnostics, I will showcase explainable AI for schizophrenia EEG that combines competitive accuracy with clinician-readable saliency maps and rigorous faithfulness tests. Together, these advances outline a practical and trustworthy pathway for neurotechnology to enhance mental-health care and human performance across clinics and real-world environments.

Chun-Shu Wei is Associate Professor of Computer Science at National Yang Ming Chiao Tung University, Taiwan, where he also holds a joint appointment in Biomedical Engineering and leads the Brain and Computational Intelligence Laboratory. He earned his PhD in Bioengineering from the University of California, San Diego in 2017 and completed a postdoctoral fellowship at Stanford University School of Medicine before returning to Taiwan in 2019. He received the U.S. National Academy of Medicine Healthy Longevity Global Grand Challenge Catalyst Award in 2024 and is a Fellow of the UK Higher Education Academy. His research explores brain-computer interfaces, neuroinformatics, deep learning for neural signals, and closed-loop neuromodulation.