EEG study design and data analysis resources
Analyzing infant EEG data is a challenging task, especially in the case of visual stimulation, because of two main factors: 1) Due to infants’ limited attentional span, the data segments during which infants effectively attend to the stimuli are very short; 2) Since infants are almost unconstrained, the most frequent artifacts are caused by a variety of movements (head, arms, frowning, sucking) which generate non-stereotyped artifacts that constantly vary in topography and temporal dynamics. Because of these factors, artifact removal for infant EEG data is an arbitrary and time-consuming task and EEG data analysis is generally more challenging compared to adult data.
For this purpose, the Baby Lab provides important resources for its users:
- For designing your study in such a way to obtain reliable EEG responses within the limited span of infant attention, you may ask advice both to the lab manager and the scientific advisor.
- For efficient, semi-automatic EEG artefact removal, check out NEAR, our new pipeline, available in the form of an EEGLAB plugin (details in the next paragraph).
- For EEG data analysis relative to both ERP and frequency-tagging designs, both at the sensor and source level, contact the lab manager for EEGLAB-based pipelines of analysis.
NEAR pipeline for newborn and infant EEG artifact removal
Recently, we proposed a pipeline called NEAR (Newborns EEG Artifact Removal) for removing artifacts from short and heavily contaminated newborn EEG data (Kumaravel et al., 2022). NEAR is compatible with the EEGLAB software and can be executed both in an automated way or semi-automated way with custom scripts. Further, NEAR supports both single-subject and group-level analysis. The software can be found in this repository.
For a step-by-step tutorial on sample newborn data collected from our lab/hospital for the study (Buiatti et al., 2019), please visit here.
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