Compromised early brain development leads to lifelong disabilities, which have a heavy impact on the child, families, as well as society as a whole. Advances in clinical care have led to an increasing number of babies surviving extreme prematurity. The global challenge is to avoid early brain injuries by optimizing neurological care during the early days of neonatal intensive care. Improved neurological care during the early stages of life improves the long term health of the infant which reduces the social and economic burden to families and, therefore, society. This aim necessitates the use of constant, cot-side brain monitoring using the electroencephalograph (EEG), which faces formidable logistic challenges due to the need for large scale data analysis by EEG experts. The only imaginable solution for dealing with the vast amount of EEG information is to automate EEG analysis. This action proposes the development of an original, automated, cot-side Analyser for Preterm EEG (APE). We are focused on two particular tasks: 1) estimation of the conceptual age of the infant and 2) detection of abnormalities in the EEG or preterm infants. This algorithm will be based on the combination of state of the art biomedical signal processing techniques and recent advances in basic developmental neuroscience. An accurate cot side EEG analyser has strong clinical potential for improving early brain care, leading to lifelong improvements in affected individuals as well as unprecedented opportunities to benchmark new brain interventions.