Premium
Feasibility of assessing brain activity using mobile, in‐home collection of electroencephalography: methods and analysis
Author(s) -
TrollerRenfree Sonya V.,
Morales Santiago,
Leach Stephanie C.,
Bowers Maureen E.,
Debnath Ranjan,
Fifer William P.,
Fox Nathan A.,
Noble Kimberly G.
Publication year - 2021
Publication title -
developmental psychobiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 93
eISSN - 1098-2302
pISSN - 0012-1630
DOI - 10.1002/dev.22128
Subject(s) - electroencephalography , data collection , computer science , artifact (error) , context (archaeology) , mobile device , human–computer interaction , data science , artificial intelligence , psychology , world wide web , paleontology , statistics , mathematics , psychiatry , biology
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.