Automated EEG-Based Schizophrenia Detection by a Feature Extraction and Machine Learning Algorithm

This study harmonizes two distinct public datasets (IBIB PAN and ASZED, encompassing 181 subjects and over 60,000 epochs) into a standardized 19-channel framework and engineers a unified tri-domain feature space fusing Power Spectral Density (PSD), Functional Connectivity (coherence and PLV), and Continuous Wavelet Transform (CWT) dynamics. A systematic evaluation across twelve feature-model combinations demonstrates that classical ensemble classifiers (Random Forest and XGBoost) achieve 98.7% accuracy and a 0.99 F1-score, confirming that lightweight, interpretable machine learning can match complex deep neural architectures for non-invasive schizophrenia screening.