The nonlinear complexity of EEG signals is believed to reflect the scale-free architecture of the neural networks in the brain. Analysis of the complexity and synchronization of EEG signals as described herein provides a quantitative measure for routine monitoring of functional brain development in infants and young children and provide a useful biomarker for detecting functional abnormalities in the brain before the cognitive, behavioral or social manifestations of these brain developments can be observed and measured by standard tests. One or more machine learning algorithms are used to discover relevant patterns in the complexity and synchronization values determined from the EEG data to facilitate risk assessment and/or diagnosis of developmental disorders in infants and young children by predicting cognitive, behavioral and social outcomes of the measured functional brain activity patterns.
METHODS AND APPARATUS FOR RISK ASSESSMENT OF DEVELOPMENTAL DISORDERS DURING EARLY COGNITIVE DEVELOPMENT