Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial.
Kira Ashton, Benjamin D Zinszer, Radoslaw M Cichy, Charles A Nelson, Richard N Aslin, Laurie Bayet
Author Information
Kira Ashton: Department of Neuroscience, American University, Washington, DC 20016, USA; Center for Neuroscience and Behavior, American University, Washington, DC 20016, USA. Electronic address: ka7150a@american.edu.
Benjamin D Zinszer: Department of Psychology, Swarthmore College, Swarthmore, PA 19081, USA.
Radoslaw M Cichy: Department of Education and Psychology, Freie Universität Berlin, 14195 Berlin, Germany.
Charles A Nelson: Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Graduate School of Education, Harvard, Cambridge, MA 02138, USA.
Richard N Aslin: Haskins Laboratories, 300 George Street, New Haven, CT 06511, USA; Psychological Sciences Department, University of Connecticut, Storrs, CT 06269, USA; Department of Psychology, Yale University, New Haven, CT 06511, USA; Yale Child Study Center, School of Medicine, New Haven, CT 06519, USA.
Laurie Bayet: Department of Neuroscience, American University, Washington, DC 20016, USA; Center for Neuroscience and Behavior, American University, Washington, DC 20016, USA.
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 has 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. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis 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.
