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: raquibul.alam@sydney.edu.au
    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: haifeng.zhao@sydney.edu.au
    affiliation: 'School of Biomedical Engineering, University of Sydney'
  - given-names: Andrew
    family-names: Goodwin
    email: a.goodwin@sydney.edu.au
    affiliation: 'School of Biomedical Engineering, University of Sydney'
  - given-names: Omid
    family-names: Kavehei
    email: omid.kavehei@sydney.edu.au
    affiliation: 'School of Biomedical Engineering, University of Sydney'
    orcid: 'https://orcid.org/0000-0002-2753-5553'
  - given-names: Alistair
    family-names: McEwan
    email: alistair.mcewan@sydney.edu.au
    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