Advanced statistical inference and machine learning for neural modelling, monitoring and imaging

The brain is a complex system and monitoring and imaging methods to observe critical neurophysiological variables underlying brain function are limited. This project works at the intersection of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological variables such as network connection strengths between neural population networks underlying brain activity. We work with different physiological data modalities such as intracranial and scalp electroencephalography and magnetoencephalography data to infer and image previously unmeasurable neurophysiological variables. This will blaze a new field for understanding brain function and will have implications for monitoring and controlling the brain with medical devices and imaging brain activity in new and important ways.