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Adaptive Supervisory Control of Epilepsy in a Neural Mass Model

When facing distinct pathological states and individuals of patients with epilepsy, traditional open-loop neurostimulation for the treatment of epilepsy needs repeated adjustment of parameters depending more on clinical experience, irrespective of the intermittent and irregular epileptic activities in the brain. In order to effectively control different cases of epilepsy back to the normal state, this work introduced a closed-loop adaptive supervisory control strategy combining the proportional control and Radial Basis Function (RBF) neural network, which can automatically adjust parameters of stimulation online via real-time feedback. Several experiments are conducted to prove the effectiveness of this control strategy based on Jansen's neural mass model. The simulation results show that this control strategy can effectively track the target electroencephalogram (EEG) of normal state among the changes in six cases of epileptic activities illustrated in this paper, which has good control accuracy and robustness when facing uncertainty of epilepsy. The contribution of this paper is to successfully suppress uncertain epileptic activities with an adaptive supervisory proportional control improved by an RBF network, which provides a critical theoretical basis for the clinical neurostimulation treatment of epilepsy.