Epilepsy is a neurological disorder resulting from a sudden development of synchronous firing in a massive group of neurons. For the particularity of the epilepsy, a neural mass model (NMM) is commonly utilized to understand and simulate the mechanism and evolution of the epilepsy. In this paper, based on a multi-coupling NMM and real EEGs of an epileptic mouse, a computational epileptic model is established to simulate the abnormal discharges of a mouse during seizures. Thus, rather than make animal experiments directly, numerical tests can be performed first. It reduces risks and helps improve the closed-loop neuromodulation. In addition, considering that no epileptic model can be utilized for neuromodulation in clinic, and even if a model exists, it still cannot describe the dynamics of the epilepsy faithfully, a scalable observer bandwidth and phase leading active disturbance rejection control (SOB-PLADRC) is proposed. Accordingly, a timelier and more accurate total disturbance estimation can be obtained by a scalable observer bandwidth and phase leading extended state observer, and an expected closed-loop neuromodulation can be realized without an accurate epileptic model. Numerical simulations based on the established model also show that the SOB-PLADRC suppresses seizures best among the PI and other active disturbance rejection approaches. More powerful disturbance rejection ability and more satisfactory closed-loop neuromodulation make the SOB-PLADRC more promising in the seizure control.
Keywords: Active disturbance rejection control; Epilepsy; Leading phase; Neural mass model; Scalable observer bandwidth.
© The Author(s), under exclusive licence to Springer Nature B.V. 2021.