Neural mass model-based tracking of anesthetic brain states

Levin Kuhlmann, Dean R. Freestone, Jonathan H. Manton, Bjorn Heyse, Hugo E. M. Vereecke, Tarmo Lipping, Michel M. R. F. Struys, David T. J. Liley

Abstract: Highlights•Physiologically motivated improvements in depth of anesthesia monitoring are needed.•Nonlinear filtering using a neural mass model concurrently tracks anesthetic brain states in human EEG and infers physiology.•Approach tracks brain states reasonably well compared to other methods and partially infers known anesthetic-induced changes.•Provides a valuable reference point to other more complex neural modeling approaches seeking to do the same.•The method presented is potentially applicable to tracking any brain state.