lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects.
Crossed random effects
EEG
Linear mixed-effects models
Mass-univariate testing
Psycholinguistics
TFCE
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 Jan 2024
01 Jan 2024
Historique:
received:
29
06
2023
revised:
26
09
2023
accepted:
21
10
2023
medline:
5
12
2023
pubmed:
27
10
2023
entrez:
26
10
2023
Statut:
ppublish
Résumé
Mixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. Here, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. lmeEEG showed excellent performance properties in terms of power and false positive rate. lmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). lmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.
Sections du résumé
BACKGROUND
BACKGROUND
Mixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models.
NEW METHOD
METHODS
Here, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG.
RESULTS
RESULTS
lmeEEG showed excellent performance properties in terms of power and false positive rate.
COMPARISON WITH EXISTING METHODS
METHODS
lmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster).
CONCLUSIONS
CONCLUSIONS
lmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.
Identifiants
pubmed: 37884082
pii: S0165-0270(23)00210-8
doi: 10.1016/j.jneumeth.2023.109991
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
109991Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None.