Infant EEG data to assess action prediction skills

The attached materials include two datasets. First, a dataset with the raw and unprocessed data. Second, a dataset with the data processed and ready to be used for final analysis. Raw_datasets: Raw data collected with EEG (Hydrocel 128, Geodesic) from 52 infants while they were watching an unfamiliar person reaching for an object. The goal of the study was to explore if infants predicted the actions of the person, indexed by a desynchronization of alpha oscillations measured over sensorimotor electrodes (left central electrodes) during the anticipatory period. Raw data needs to be processed to remove artifacts, segmented in epochs, and transformed to the time-frequency domain to measure changes in alpha power. Processed: Processed and artifact-free event-related EEG data transformed to time-frequency. The MADE pipeline was used to process, clean, and segment the data. The function newtimef from EEGLab was used to transform the data to time-frequency.