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CITATION.cff
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cff-version: 1.2.0
title: >-
Differences in Power Spectral Densities and Phase
Quantities Due to Processing of EEG Signals
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Raquib Ul
family-names: Alam
email: [email protected]
affiliation: >-
School of Electrical and Information Engineering,
University of Sydney
orcid: 'https://orcid.org/0000-0001-8243-255X'
- given-names: Haifeng
family-names: Zhao
email: [email protected]
affiliation: 'School of Biomedical Engineering, University of Sydney'
- given-names: Andrew
family-names: Goodwin
email: [email protected]
affiliation: 'School of Biomedical Engineering, University of Sydney'
- given-names: Omid
family-names: Kavehei
email: [email protected]
affiliation: 'School of Biomedical Engineering, University of Sydney'
orcid: 'https://orcid.org/0000-0002-2753-5553'
- given-names: Alistair
family-names: McEwan
email: [email protected]
affiliation: 'School of Biomedical Engineering, University of Sydney'
orcid: 'https://orcid.org/0000-0001-7597-6372'
identifiers:
- type: doi
value: 10.3390/s20216285
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.
keywords:
- electroencephalography
- spectral analysis
- visual evoked potentials
- pipelines
- correlations