Cognitive impairment is associated with characteristic alterations in neural dynamics that can be measured non-invasively using resting-state EEG. In this talk, I will present recent advances in quantifying these changes using EEG periodic components, complexity measures, microstates, higher-order interactions, and explainable AI. I will discuss how these EEG features relate to cognition and clinical symptoms. Taken together, these approaches illustrate the potential role of EEG-derived measures in supporting early detection and longitudinal monitoring of cognitive impairment.

Hamed Azami, PhD, is a Scientific Associate at the Centre for Addiction and Mental Health (CAMH) and an affiliate with the University of Toronto, specializing in biomedical signal processing and AI. He received his PhD in biomedical signal processing from the University of Edinburgh in 2018 and completed a two-year postdoctoral research fellowship at Harvard University. His research focuses on health data science, information theory, and explainable AI in neurological and mental disorders. Dr. Azami received the Nightingale Award for the best paper in Medical and Biological Engineering and Computing and serves as an Associate Editor for IEEE Access and the Journal of Alzheimer’s Disease.