{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:44:59Z","timestamp":1773967499564,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T00:00:00Z","timestamp":1604448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013Harris window), EEG time window choices (\u2212750 ms to 0 ms and \u2212250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch\u2019s method, Fast Fourier Transform, and Burg\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20216285","type":"journal-article","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T10:32:36Z","timestamp":1604485956000},"page":"6285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8243-255X","authenticated-orcid":false,"given":"Raquib-ul","family":"Alam","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia"}]},{"given":"Haifeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia"}]},{"given":"Andrew","family":"Goodwin","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-5553","authenticated-orcid":false,"given":"Omid","family":"Kavehei","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia"},{"name":"The University of Sydney Nano Institute, The University of Sydney, Sydney, NSW 2006, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7597-6372","authenticated-orcid":false,"given":"Alistair","family":"McEwan","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/0168-5597(91)90037-X","article-title":"Pre-stimulus spectral EEG patterns and the visual evoked response","volume":"80","author":"Brandt","year":"1991","journal-title":"Electroencephalogr. 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