This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application.

中文翻译：

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基于非线性自回归移动平均Volterra模型的癫痫发作抑制模型预测控制

本文研究基于模型预测控制策略的闭环脑刺激方法，以抑制癫痫发作。通过改变生理上有意义的参数来表现正常和各种癫痫发作的神经质量模型（NMM）被用作大脑的黑匣子模型。基于系统识别，建立了自回归移动平均Volterra模型，以揭示刺激与神经元反应之间的关系。然后，基于Volterra模型实施模型预测控制策略，该模型可以生成最佳刺激波形以消除癫痫波。计算仿真结果表明，提出的闭环控制策略可以优化刺激波形，而无需特别了解系统的生理特性。