Comparative Analysis of Machine Learning and Hybrid Deep Learning Algorithm for Schizophrenia Detection using EEG Signals

Schizophrenia, a complex mental health disorder, poses significant challenges for accurate and efficient diagnosis. Current diagnostic methods often lack the precision required for early intervention, relying on subjective assessments that may lead to delayed or inaccurate results. EEG signals have emerged as potential indicators of schizophrenia, but the field lacks a comprehensive comparative analysis of machine learning and hybrid deep learning algorithms tailored specifically for EEG signal-based diagnosis. The existing gap in research highlights the need for a systematic exploration of the effectiveness of traditional machine learning algorithms in contrast to hybrid deep learning approaches. While machine learning algorithms have been applied to feature extraction from EEG signals, the potential enhancement brought about by integrating brain-effective connectivity in a hybrid deep learning framework remains under-explored.