Electroencephalogram (EEG) signal complexity quantifies the brain dynamic and yields many different features to diagnose many psychotic disorders. The aim of this work is to analyze EEG to detect schizophrenia in comparison with normal subjects using EEG signal complexity, at various conditions such as amplitude dynamics of electroencephalogram (EEG) oscillations. Neuronal oscillations reflect the activity of neuronal ensembles engaged in integrative cognition, and may serve as a functional measure for the cognitive impairment in schizophrenia. This project aims to reveal the abnormal amplitude dynamics of electroencephalogram (EEG) data acquisition process feasible and quick for most patients. The highest classification accuracy 88.5% is obtained when features of both stimulus are considered together. This work suggests that the EEG signal oscillations in schizophrenia patients on multiple time scales. This article presents the Dyadic wavelet transformation, a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the complexity during mental activity can be used to identify schizophrenia subjects.