PT  - JOURNAL ARTICLE
AU  - Kira Ashton
AU  - Benjamin D. Zinszer
AU  - Radoslaw M. Cichy
AU  - Charles A. Nelson III
AU  - Richard N. Aslin
AU  - Laurie Bayet
TI  - Time-resolved multivariate pattern analysis of infant EEG data
AID  - 10.1101/2021.06.16.448720
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.16.448720
4099  - http://biorxiv.org/content/early/2021/06/17/2021.06.16.448720.short
4100  - http://biorxiv.org/content/early/2021/06/17/2021.06.16.448720.full
AB  - Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA methods have recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. A pipeline for time-resolved MVPA based on linear SVM classification is described and implemented with accompanying code in both Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above chance classification accuracy. Extensions of the core pipeline are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.Competing Interest StatementThe authors have declared no competing interest.