Background: Automated computational measures of EEG have the potential for large-scale application. We hypothesised that a predefined measure of early EEG-burst shape (increased burst sharpness) could predict neurodevelopmental impairment (NDI) and mental developmental index (MDI) at 2 years of age over-and-above that of brain ultrasound. Methods: We carried out a secondary analysis of data from extremely preterm infants collected for an RCT (SafeBoosC-II). Two hours of single-channel cross-brain EEG was used to analyse burst sharpness with an automated algorithm. The co-primary outcomes were moderate-or-severe NDI and MDI. Complete data were available from 58 infants. A predefined statistical analysis was adjusted for GA, sex and no, mild–moderate, and severe brain injury as detected by cranial ultrasound. Results: Nine infants had moderate-or-severe NDI and the mean MDI was 87 ± 17.3 SD. The typical burst sharpness was low (negative values) and varied relatively little (mean –0.81 ± 0.11 SD), but the odds ratio for NDI was increased by 3.8 (p = 0.008) and the MDI was reduced by –3.2 points (p = 0.14) per 0.1 burst sharpness units increase (+1 SD) in the adjusted analysis. Conclusion: This study confirms the association between EEG-burst measures in preterm infants and neurodevelopment in childhood. Importantly, this was by a priori defined analysis. Impact: A fully automated, computational measure of EEG in the first week of life was predictive of neurodevelopmental impairment at 2 years of age.This confirms many previous studies using expert reading of EEG.Only single-channel EEG data were used, adding to the applicability.EEG was recorded by several different devices thus this measure appears to be robust to differences in electrodes, amplifiers and filters.The likelihood ratio of a positive EEG test, however, was only about 2, suggesting little immediate clinical value.