Spectral, phase, and their interacting components for complexity analysis of depression electroencephalogram

Depression is a severe mental disorder, and patients suffering from depression differ significantly from those in the control group in terms of electroencephalogram (EEG) signal complexity. Although most of the existing studies have focused on overall complexity analysis, very few have explored the complexity characteristics from a decomposition perspective. In this paper, we propose to apply the fast Fourier transform to the decomposition method to resolve the nonlinear feature differences (total differences, TDs) between task and resting eye-open states and decompose them into spectral terms (STs), phase terms (PTs), and spectral interaction terms (SITs). The selected nonlinear features include Lempel–Ziv complexity (LZC), permutation entropy (PE), and basic scale entropy (BSE). The experimental data were obtained from public datasets on the OpenNeuro website, including the depression resting-state and task-state datasets, involving EEG data from 46 depressed patients and 74 controls. The results indicated that TDs and STs were significantly lower in the depressed group than in the control group in the frontal region, while the SIT was significantly higher in the frontal region and lower in the central region. The PT in the depressed group was lower in the frontal region but higher in the central and temporal regions. In addition, the two groups exhibited opposite trends in the SIT across the two states. Entropy decomposition of the LZC, PE, and BSE differences effectively differentiated depressed patients, with BSE differences distinguishing the highest number of channels. These results may provide an important reference for the clinical diagnosis and treatment of depression.