神经群模型中癫痫状棘波的闭环控制性能研究

神经群模型可模拟产生癫痫发作间歇期、发作前期和发作期的脑电信号.本文基于代数估计法,给出一种新型的闭环反馈控制策略以消除神经群模型中的癫痫状棘波.代数估计法用以观测模型中的状态以进一步构造控制器.在多个神经群耦合的模型中,通过数值仿真研究了与所给的闭环反馈控制策略相关的一些特性,包括受控神经群的类型与消除棘波的能力之间的关系、受控神经群的数目与控制能量之间的关系、模型的参量和控制能量之间的关系,以期建立合适的控制规则实现利用尽可能小的控制能量消除癫痫状棘波.此外,通过数值仿真对基于代数估计法的闭环反馈控制策略和直接比例反馈控制策略进行比较,结果表明,利用代数估计法进行滤波能减少消除癫痫状棘波所需的控制能量.

Performance of closed-loop control of epileptiform spikes in neural mass models

Neural mass models can produce electroencephalography (EEG) like signals corresponding to interical, pre-ictal and ictal activities. In this paper, a novel closed-loop feedback control strategy based on algebraic estimation is proposed to eliminate epileptiform spikes in neural mass models. Algebraic estimation plays a role in observing the states of the model in order to construct the controller. For a network of coupled neural populations, the characteristics regarding the closed-loop feedback control strategy, including the relationship between the type of controlled populations and the ability of eliminating epileptiform spikes, the relationship between the number of controlled populations and control energy, the relationship between the model parameters and control energy, are determined by numerical simulations. The purpose is to establish the rules for the proper control of eliminating epileptiform spikes with as less control energy as possible. Moreover, the proposed control-loop control strategy is compared with a direct proportional feedback control strategy by numerical simulations. It is shown that the use of algebraic estimation makes a reduction of control energy.