Automated Multiclass EEG Signal Classification for Early Diagnosis of Psychiatric Disorders via Hybrid Graph Neural Networks

Abstract

Neurological and psychiatric disorders are emerging as major global health concerns, making early and accurate diagnosis critical for effective treatment and improved patient outcomes. Electroencephalography (EEG) signals offer a non-invasive and highly informative method for studying brain activity. However, due to their high dimensionality, noise, and complex nonlinear patterns, EEGbased automated classification remains a challenging task. Many existing diagnostic models struggle with issues such as limited generalization across subjects, inability to effectively capture nonlinear relationships, and reduced classification accuracy, which impacts their clinical reliability. To overcome these limitations, this study proposes a multiclass machine learning framework for the simultaneous classification of epilepsy, migraine, and schizophrenia using EEG data. The framework begins with a structured preprocessing pipeline that includes data cleaning, missing value imputation, normalization, and label encoding to ensure data quality and consistency. The dataset is then divided using stratified train–test splitting to maintain class balance and prevent bias during model training. Several machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT), are implemented and evaluated. In addition, a Hybrid Graph Neural Network (HGNN) approach is introduced, where a Tree-based Generalized Additive Model (TreeGAM) is utilized to capture complex nonlinear interactions among EEG features more effectively.