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The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms

Steady-state visual evoked potential (ssVEP) frequency tagging is an increasingly used method in electrophysiological studies of visual attention and perception. Frequency tagging is suitable for studies examining a wide range of populations, including infants and children. Frequency tagging involve...

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Detalles Bibliográficos
Autores principales: Figueira, Jessica Sanches Braga, Kutlu, Ethan, Scott, Lisa S., Keil, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861396/
https://www.ncbi.nlm.nih.gov/pubmed/35184025
http://dx.doi.org/10.1016/j.dcn.2022.101066
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author Figueira, Jessica Sanches Braga
Kutlu, Ethan
Scott, Lisa S.
Keil, Andreas
author_facet Figueira, Jessica Sanches Braga
Kutlu, Ethan
Scott, Lisa S.
Keil, Andreas
author_sort Figueira, Jessica Sanches Braga
collection PubMed
description Steady-state visual evoked potential (ssVEP) frequency tagging is an increasingly used method in electrophysiological studies of visual attention and perception. Frequency tagging is suitable for studies examining a wide range of populations, including infants and children. Frequency tagging involves the presentation of different elements of a visual array at different temporal rates, thus using stimulus timing to “tag” the brain response to a given element by means of a unique time signature. Leveraging the strength of the ssVEP frequency tagging method to isolate brain responses to concurrently presented and spatially overlapping visual objects requires specific signal processing methods. Here, we introduce the FreqTag suite of functions, an open source MATLAB toolbox. The purpose of the FreqTag toolbox is three-fold. First, it will equip users with a set of transparent and reproducible analytical tools for the analysis of ssVEP data. Second, the toolbox is designed to illustrate fundamental features of frequency domain and time-frequency domain approaches. Finally, decision criteria for the application of different functions and analyses are described. To promote reproducibility, raw algorithms are provided in a modular fashion, without additional hidden functions or transformations. This approach is intended to facilitate a fundamental understanding of the transformations and algorithmic steps in FreqTag, and to allow users to visualize and test each step in the toolbox.
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spelling pubmed-88613962022-03-02 The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms Figueira, Jessica Sanches Braga Kutlu, Ethan Scott, Lisa S. Keil, Andreas Dev Cogn Neurosci Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius Steady-state visual evoked potential (ssVEP) frequency tagging is an increasingly used method in electrophysiological studies of visual attention and perception. Frequency tagging is suitable for studies examining a wide range of populations, including infants and children. Frequency tagging involves the presentation of different elements of a visual array at different temporal rates, thus using stimulus timing to “tag” the brain response to a given element by means of a unique time signature. Leveraging the strength of the ssVEP frequency tagging method to isolate brain responses to concurrently presented and spatially overlapping visual objects requires specific signal processing methods. Here, we introduce the FreqTag suite of functions, an open source MATLAB toolbox. The purpose of the FreqTag toolbox is three-fold. First, it will equip users with a set of transparent and reproducible analytical tools for the analysis of ssVEP data. Second, the toolbox is designed to illustrate fundamental features of frequency domain and time-frequency domain approaches. Finally, decision criteria for the application of different functions and analyses are described. To promote reproducibility, raw algorithms are provided in a modular fashion, without additional hidden functions or transformations. This approach is intended to facilitate a fundamental understanding of the transformations and algorithmic steps in FreqTag, and to allow users to visualize and test each step in the toolbox. Elsevier 2022-02-11 /pmc/articles/PMC8861396/ /pubmed/35184025 http://dx.doi.org/10.1016/j.dcn.2022.101066 Text en © 2022 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius
Figueira, Jessica Sanches Braga
Kutlu, Ethan
Scott, Lisa S.
Keil, Andreas
The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
title The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
title_full The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
title_fullStr The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
title_full_unstemmed The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
title_short The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
title_sort freqtag toolbox: a principled approach to analyzing electrophysiological time series in frequency tagging paradigms
topic Articles from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists: A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861396/
https://www.ncbi.nlm.nih.gov/pubmed/35184025
http://dx.doi.org/10.1016/j.dcn.2022.101066
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