Data-driven modeling of neural dynamics from EEG to track physiological changes

A key challenge in studying brain function is gaining insight into the mechanisms which drive neural activity. In this paper, we seek to address this challenge by developing a framework for generative, individualized models based on EEG data which can give insight into the neural functions which drive observed electrophysiological activity. The models created using this framework are accurate, reliable, individualized, and capable of tracking changes in neural activity during the physiological changes occurring during cardiac arrest. Due to the biophysical significance of the model structure, we can gain insight into the mechanisms driving these changes in neural activity, e.g., lowered excitatory inputs across the brain.