Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

Abstract

There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman–Harris window), EEG time window choices (−750 ms to 0 ms and −250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch’s method, Fast Fourier Transform, and Burg’s method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring

“…As indicated, both APA1 and APA2 were more negative during stress than at rest and meditation which demonstrate that the right alpha power was reduced considerably more than the left alpha power during stress situation. These results are in line with the physiological expectations that increased cortical activity in the right hemisphere is associated with processing of negative emotions such as stress [16] , [32] , [33] .…”

“…The EEG data computation starts by selecting the data from the four electrodes: two electrodes in the left-hemisphere (F3, Fp1), and two electrodes in the right-hemisphere (F4, Fp2) in reference to electrode Fz. For power spectral density (PSD) estimation, the Welch’s method, a nonparametric method which is a modified approach of Fast Fourier Transform (FFT) algorithm [33] was used to classify the signals based on frequency into five frequency bands: Delta (0.2 – 3 Hz), Theta (3 – 8 Hz), Alpha (8 – 13 Hz), Beta (13 – 30 Hz) and Gamma from 30 Hz and up. The alpha band power asymmetry between the left and right hemisphere was calculated using the equation in Table 2 .…”

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

“…Our results support the claims made in the literature [33,48,68,69] that the automated ICA-artifact preprocessing pipeline offered substantial benefits by increasing consistency and efficiency for classifying artifacts or non-artifacts ICs. On the contrary, Alam et al [49] have found that the selection of artifact removal had distinct effects on the PSD calculation. However, the recent findings in Noor et al [40] showed that the artifacts rejection by the automatic continuous rejection and experts confirmation provided promising results in predicting TBI outcomes of specific frequency bands.…”

“…Artifacts in EEG signals might make interpretation difficult and lead to incorrect analytical judgments. Numerous algorithms and preprocessing pipelines have been developed to address the problem of artifact rejection in electrophysiological data [32,49,[63][64][65][66][67]. Each of these algorithms has its own set of strengths and focuses on a different area of artifact rejection than the others.…”

“…PSD is a frequently extracted characteristic that quantifies the power contained within a frequency domain signal. It is similar to the FT of a signal's auto-correlation function [19,49]. Therefore, each EEG sub-band had an average of 63-PSD in one segment.…”