Taxonomy driven empirical evaluation of EEG neural complexity measures across the lifespan

Neural complexity is increasingly used to characterize large scale brain dynamics, but the field includes many metrics whose interpretation and comparative validity remain unclear. This project evaluates EEG neural complexity measures within a taxonomy driven analytical framework that groups metrics according to their computational assumptions. Chronological age provides a biologically meaningful reference dimension for this comparison, as lifespan related changes in EEG dynamics offer a robust empirical context in which to test whether different complexity metrics capture shared or distinct aspects of neural activity. The project will analyse resting state EEG data from large public datasets spanning from childhood to late adulthood. Representative complexity measures will be sampled across the main computational categories of the taxonomy and compared with conventional EEG spectral features. The study will test whether different complexity metrics show convergent age related effects, whether they can predict chronological age using supervised models, and whether they provide added predictive value beyond spectral power. The expected outcome is to clarify whether EEG complexity measures capture complementary aspects of neural dynamics or largely redundant information about brain function and aging.