Robust control and state observer design for neural mass model applications using simulated EEG signals

The paper presents, on the one hand, the design of a robust control method using Hoc, tools applied to a nonlinear neural mass model of a cortical column using EEG recordings as signal measurements. The objective of the control problem is to suppress the neuronal activity of the cortical column by ensuring guaranteed performance specifications as well as robustness against model uncertainties and measurement noise. On the other hand, to monitor the hidden, unmeasured, activity of a cortical column an Extended Kalman Filter is designed based on the neural mass model of the macrocolumn and EEG measurements of its activity. The capabilities of these methods are tested, in simulation, using the neural mass model description of a cortical column for an epileptic seizure. Both methods, the robust controller and the state observer, show promising results in simulation.