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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.
The neural group model can simulate the EEG signals of seizure onset, pre-seizure and exacerbation.In this paper, based on the algebraic estimation method, a new closed-loop feedback control strategy is proposed to eliminate the epileptic spikes in the neural model. The estimation method is used to observe the state of the model to further construct the controller.In the model of multiple neural couplings, some characteristics related to the given closed-loop feedback control strategy are studied by numerical simulation, including the type of controlled nerve group And the ability to eliminate spikes, the relationship between the number of controlled nerve groups and control energy, the relationship between model parameters and control energy, in order to establish appropriate control rules to achieve the use of as little as possible control of energy In addition, the closed-loop feedback control strategy based on algebraic estimation and the direct proportional feedback control strategy are compared by numerical simulation. The results show that the algebraic estimation method can reduce the control needed to eliminate epileptic spikes energy.