A Neuro-Physiological Diffusion Model for Accurate EEG-Based Psychiatric Disorder Classification

Gopal, Pradeep
M, Abbinayaa
Subashini, S
Nagaraj, Mathivanan
Banu, N Nasiya Niwaz
Thumbur, Gowri

Journal of Electronics, Electromedical Engineering, and Medical Informatics
Vol 8 No 1 (2026): January

Identification of psychiatric conditions such as depression, schizophrenia, anxiety, and obsessive-compulsive disorder (OCD) from Electroencephalography (EEG) data remains a significant challenge due to the complexity of neurophysiological patterns. While Generative Adversarial Networks (GANs) have been explored to augment EEG datasets and enhance classifier performance, they often suffer from limitations including training instability, mode collapse, and the generation of physiologically implausible EEG samples. These shortcomings hinder their applicability in high-stakes clinical decision-making, where reliability and physiological coherence are critical. This study aims to address the above-mentioned challenges by proposing a novel Neuro-Physiologically Constrained Diffusion Framework (NPC-DiffEEG). This framework leverages the strengths of conditional diffusion models while integrating domain-specific neurophysiological constraints, ensuring that generated EEG signals preserve key properties, such as frequency band structures and inter-channel connectivity patterns, both of which are essential for accurate mental disorder classification. The NPC-DiffEEG-generated data is combined with real EEG features and processed using a multi-task attention-based transformer, enabling the model to learn robust, cross-disorder representations. Extensive experiments conducted on a publicly available multi-disorder EEG dataset demonstrate that NPC-DiffEEG significantly outperforms traditional GAN-based augmentation approaches. The model achieves an impressive average classification accuracy of 96.8%, along with superior F1-scores and AUC values across all disorder categories. Furthermore, integrating attention-based disorder attribution not only enhances interpretability but also reduces overfitting, thereby improving generalizability to unseen subjects. This innovative approach marks a substantial advancement in EEG-based classification of psychiatric disorders, bridging the gap between synthetic data generation and clinically reliable decision-support systems.