Literature DB >> 34087950

Feasibility of assessing brain activity using mobile, in-home collection of electroencephalography: methods and analysis.

Sonya V Troller-Renfree1, Santiago Morales2, Stephanie C Leach2, Maureen E Bowers2, Ranjan Debnath3, William P Fifer4, Nathan A Fox2, Kimberly G Noble1.   

Abstract

The last decade has seen increased availability of mobile electroencephalography (EEG). These mobile systems enable researchers to conduct data collection "in-context," reducing participant burden and potentially increasing diversity and representation of research samples. Our research team completed in-home data collection from more than 400 twelve-month-old infants from low-income backgrounds using a mobile EEG system. In this paper, we provide methodological and analytic guidance for collecting high-quality, mobile EEG in infants. Specifically, we offer insights and recommendations for equipment selection, data collection, and data analysis, highlighting important considerations for selecting a mobile EEG system. Examples include the size of the recording equipment, electrode type, reference types, and available montages. We also highlight important recommendations surrounding preparing a nonstandardized recording environment for EEG collection, obtaining informed consent from parents, instructions for parents during capping and recording, stimuli and task design, training researchers, and monitoring data as it comes in. Additionally, we provide access to the analysis code and demonstrate the robustness of the data from a recent study using this approach, in which 20 artifact-free epochs achieve good internal consistency reliability. Finally, we provide recommendations and publicly available resources for future studies aiming to collect mobile EEG.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  EEG; development; in-home EEG; mobile EEG; portable EEG

Mesh:

Year:  2021        PMID: 34087950      PMCID: PMC8478406          DOI: 10.1002/dev.22128

Source DB:  PubMed          Journal:  Dev Psychobiol        ISSN: 0012-1630            Impact factor:   2.531


  14 in total

1.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

Authors:  Arnaud Delorme; Scott Makeig
Journal:  J Neurosci Methods       Date:  2004-03-15       Impact factor: 2.390

2.  FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.

Authors:  H Nolan; R Whelan; R B Reilly
Journal:  J Neurosci Methods       Date:  2010-07-21       Impact factor: 2.390

Review 3.  Mobile EEG in epilepsy.

Authors:  Jessica Askamp; Michel J A M van Putten
Journal:  Int J Psychophysiol       Date:  2013-09-20       Impact factor: 2.997

4.  Wireless EEG with individualized channel layout enables efficient motor imagery training.

Authors:  Catharina Zich; Maarten De Vos; Cornelia Kranczioch; Stefan Debener
Journal:  Clin Neurophysiol       Date:  2014-07-14       Impact factor: 3.708

Review 5.  Dry EEG electrodes.

Authors:  M A Lopez-Gordo; D Sanchez-Morillo; F Pelayo Valle
Journal:  Sensors (Basel)       Date:  2014-07-18       Impact factor: 3.576

6.  Adjusting ADJUST: Optimizing the ADJUST algorithm for pediatric data using geodesic nets.

Authors:  Stephanie C Leach; Santiago Morales; Maureen E Bowers; George A Buzzell; Ranjan Debnath; Daniel Beall; Nathan A Fox
Journal:  Psychophysiology       Date:  2020-03-17       Impact factor: 4.016

7.  Socioeconomic status and functional brain development - associations in early infancy.

Authors:  Przemyslaw Tomalski; Derek G Moore; Helena Ribeiro; Emma L Axelsson; Elizabeth Murphy; Annette Karmiloff-Smith; Mark H Johnson; Elena Kushnerenko
Journal:  Dev Sci       Date:  2013-08-07

Review 8.  Research Review: Use of EEG biomarkers in child psychiatry research - current state and future directions.

Authors:  Sandra K Loo; Agatha Lenartowicz; Scott Makeig
Journal:  J Child Psychol Psychiatry       Date:  2015-06-23       Impact factor: 8.265

9.  The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data.

Authors:  Laurel J Gabard-Durnam; Adriana S Mendez Leal; Carol L Wilkinson; April R Levin
Journal:  Front Neurosci       Date:  2018-02-27       Impact factor: 4.677

Review 10.  Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges.

Authors:  Alex Lau-Zhu; Michael P H Lau; Gráinne McLoughlin
Journal:  Dev Cogn Neurosci       Date:  2019-03-08       Impact factor: 6.464

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  3 in total

Review 1.  Brains in Sync: Practical Guideline for Parent-Infant EEG During Natural Interaction.

Authors:  Elise Turk; Yaara Endevelt-Shapira; Ruth Feldman; Marion I van den Heuvel; Jonathan Levy
Journal:  Front Psychol       Date:  2022-04-27

2.  The impact of a poverty reduction intervention on infant brain activity.

Authors:  Sonya V Troller-Renfree; Molly A Costanzo; Greg J Duncan; Katherine Magnuson; Lisa A Gennetian; Hirokazu Yoshikawa; Sarah Halpern-Meekin; Nathan A Fox; Kimberly G Noble
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-01       Impact factor: 12.779

3.  HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings.

Authors:  K L Lopez; A D Monachino; S Morales; S C Leach; M E Bowers; L J Gabard-Durnam
Journal:  Neuroimage       Date:  2022-07-08       Impact factor: 7.400

  3 in total

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