Speaker
Description
Abstract—The integration of biophysically grounded neural
simulations with Artificial Intelligence (AI) has the potential to
transform clinical neurodiagnostics by overcoming the inherent
challenges of limited pathological EEG datasets. We present a
novel AI-driven framework that leverages a Distributed-Delay
Neural Mass Model (DD-NMM) to generate synthetic EEG
signals replicating both healthy and pathological brain states.
Through systematic parameter tuning and domain-specific
data augmentation, we enrich the diversity of simulated
signals, enabling robust anomaly detection using machine
learning techniques. Our approach integrates supervised
classification and unsupervised one-class anomaly detection,
achieving over 95% accuracy in synthetic tests and over 89%
when applied to real EEG data from epilepsy patients and
healthy volunteers. By providing an engineered solution that
bridges computational neuroscience with AI, this framework
enhances early seizure detection, adaptive neurofeedback,
and brain-computer interface applications. Our results
demonstrate that theory-driven simulation, combined with
state-of-the-art machine learning, can address critical gaps
in medical AI, significantly advancing clinical neuroengineering.
Clinical relevance— This study provides a scalable and interpretable
AI-driven method for EEG anomaly detection, which
can support clinicians in identifying seizure patterns and other
neurological disorders with high accuracy. The integration of
computational neuroscience with AI-based diagnostics offers
a potential pathway for early intervention and personalized
neurotherapeutic strategies.
