Modeling EEG with axon delay times to analyze individual differences in cognition

Electroencephalography (EEG) is a fundamental tool in neuroscience, offering key insights into the complex workings of the brain. This study introduces a global model for EEG analysis based on a stochastic autoregressive framework derived from established models of neural behavior. While it is typically thought that EEG frequency bands emerge from synchronous synaptic activity, the global model of EEG states that delays in axonal propagation across corticocortical and thalamocortical connections significantly contribute to the variance observed in EEG signals. The present model predicts that spectral peaks in scalp-recorded EEG data can be solely attributed to axonal time delays at various distances. The autoregressive models are notable for their linear structure that efficiently captures temporal relationships within EEG signals, highlighting the impact of axonal propagation delays with greater computational efficiency. The model employs a connectivity atlas to determine the connectivity and distances between various brain regions. Additionally, it incorporates distributions of axonal delays and Event-Related Potentials (ERPs) in response to visual stimuli. The approach allows for an accurate reproduction of EEG power spectra, including both resting-state alpha rhythms and ERP peaks. The findings suggest that axonal delay times and neural connectivity within linear predictive models influence EEG dynamics, offering a method to analyze individual cognitive variations through EEG data. In the future, we aim to apply these models alongside cognitive frameworks to draw inferences about individual variations in neurocognition.