  Historically, the analysis of stimulus-dependent time-frequency patterns has
been the cornerstone of most electroencephalography (EEG) studies. The abnormal
oscillations in high-frequency waves associated with psychotic disorders during
sensory and cognitive tasks have been studied many times. However, any
significant dissimilarity in the resting-state low-frequency bands is yet to be
established. Spectral analysis of the alpha and delta band waves shows the
effectiveness of stimulus-independent EEG in identifying the abnormal activity
patterns of pathological brains. A generalized model incorporating multiple
frequency bands should be more efficient in associating potential EEG
biomarkers with First-Episode Psychosis (FEP), leading to an accurate
diagnosis. We explore multiple machine-learning methods, including
random-forest, support vector machine, and Gaussian Process Classifier (GPC),
to demonstrate the practicality of resting-state Power Spectral Density (PSD)
to distinguish patients of FEP from healthy controls. A comprehensive
discussion of our preprocessing methods for PSD analysis and a detailed
comparison of different models are included in this paper. The GPC model
outperforms the other models with a specificity of 95.78% to show that PSD can
be used as an effective feature extraction technique for analyzing and
classifying resting-state EEG signals of psychiatric disorders.
