News

A new international study co-authored by Professor Xiaodong Chen from the Centre for Electronics at Queen Mary University of London has demonstrated how brainwave analysis powered by artificial intelligence could transform the early diagnosis of cognitive disorders. The paper, published in Expert Systems with Applications (Elsevier, 2025), presents a pioneering EEG-based biomarker system that identifies subtle neural changes long before visible symptoms appear.

Titled "Electroencephalographic Biomarker-Guided Early Detection Using Physics-Informed Neural Networks", the research combines neuroscience, data science, and advanced engineering to extract meaningful patterns from complex EEG signals. By applying a physics-informed neural network, the team achieved remarkable accuracy in detecting early signs of cognitive decline, potentially paving the way for earlier interventions and more effective treatment.

Professor Xiaodong Chen played a central role in the study's engineering and signal-processing innovations. Drawing on his expertise in electromagnetics and bio-sensing technologies, he helped ensure the precision and reliability of EEG data acquisition and guided the development of robust signal models that could handle real-world noise and variability. His contributions bridge the gap between raw sensor data and actionable medical insights; a key step in translating laboratory findings into clinical practice.

The study's approach could lead to low-cost, non-invasive screening tools for use in hospitals or even wearable health devices. This research highlights Queen Mary's growing impact in AI-driven healthcare innovation and demonstrates how interdisciplinary collaboration between engineers, clinicians, and data scientists can accelerate medical breakthroughs.

Read the paper here: www.sciencedirect.com/science/article/abs/pii/S0957417425...