Daniel Polyakov

This paper introduces a computational method to generate personalized brain stimulation signals for disorders of consciousness, showing in silico that it can induce healthy-like EEG power spectra.

This study applies neural field theory to EEG data to identify correlations between model parameters and consciousness states, highlighting potential biomarkers and physiological bases.

The paper shows that predictive feedback and fast responses to stimuli depend on NMDA receptors, using EEG, Bayesian modeling, and machine learning in an audio-visual task.