We present a de-identified dataset of artefact-free sleep-wake resting electroencephalography (EEG) epochs from 118 neonates (34–43 corrected gestational weeks at study). EEG data are sampled at 2000Hz. The infants’ demographic data include sex, weight and gestational age at birth to the nearest 100 grams and whole week respectively (to ensure anonymity), and postnatal age at study in days. The dataset can be used to investigate sleep-wake cortical dynamics in human infants. The majority of these data were used in analyses published in Whitehead et al. 2018 Sleep (10.1038/s41598-018-35850-1) and/or Whitehead et al. 2019 Early Human Development (10.1016/j.earlhumdev.2019.07.007). You must cite these papers if you use these data. These data were collected during work supported by the Medical Research Council (MR/L019248/1; awardee Dr Lorenzo Fabrizi). We also acknowledge the support of the UCL/UCLH Biomedical Research Centre. We would like to thank the families who participated in our neonatal brain activity research program. More information about Dr Lorenzo Fabrizi's and Dr Kimberley Whitehead's research programmes are available at: https://profiles.ucl.ac.uk/1290-lorenzo-fabrizi and https://kclpure.kcl.ac.uk/portal/en/persons/kimberley.whitehead. They are both committed to the principles of open science, and co-chair an EPSRC/MRC-funded Respect4Neurodevelopment EEG reliability taskforce designed to facilitate these (https://respect4neurodevelopment.com/wp-content/uploads/2023/06/Reliability-Members-R4N-June-2023.pdf). Dr Whitehead is also a member of the COST-funded initiative Artificial Intelligence for the Neonatal Intensive Care Unit (AI-4-NICU), which similarly furthers transparency and reproducibility in neonatal research (www.cost.eu/actions/CA20124/#tabs+Name:Working%20Groups%20and%20Membership).