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

Jessica Sanches Braga Figueira; Ethan Kutlu; Lisa S. Scott; Andreas Keil

doi:10.1016/j.dcn.2022.101066

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

Journal article

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

Jessica Sanches Braga Figueira, Ethan Kutlu, Lisa S. Scott and Andreas Keil

Developmental cognitive neuroscience, Vol.54, pp.101066-101066

04/01/2022

DOI: 10.1016/j.dcn.2022.101066

PMCID: PMC8861396

PMID: 35184025

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https://doi.org/10.1016/j.dcn.2022.101066View

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Abstract

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.

A Tutorial Approach; Edited by George Buzzell; Emilio Valadez; Santiago Morales; Nathan Fox; Sabine Hunnius

FreqTag

Frequency domain

Frequency tagging

from the Special Issue on EEG Methods for Developmental Cognitive Neuroscientists

MATLAB

Steady-state visual evoked potential (ssVEP)

Time-frequency domain

Details

Title: Subtitle: The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms
Creators: Jessica Sanches Braga Figueira - Center for the Study of Emotion and Attention, University of Florida, Gainesville, FL, USA
Ethan Kutlu - University of Florida
Lisa S. Scott - University of Florida
Andreas Keil - University of Florida
Resource Type: Journal article
Publication Details: Developmental cognitive neuroscience, Vol.54, pp.101066-101066
DOI: 10.1016/j.dcn.2022.101066
PMID: 35184025
PMCID: PMC8861396
NLM abbreviation: Dev Cogn Neurosci
ISSN: 1878-9293
eISSN: 1878-9307
Publisher: Elsevier
Grant note: DOI: 10.13039/100000071, name: National Institute of Child Health and Human Development, award: R21HD102715–01; DOI: 10.13039/100000001, name: National Science Foundation, award: 1728133
Language: English
Date published: 04/01/2022
Academic Unit: Linguistics; Psychological and Brain Sciences; Center for Social Science Innovation
Record Identifier: 9984446419902771

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