UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model

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• 作者：Bonan Shan (1)
  Jiang Wang (1)
  Bin Deng (1)
  Xile Wei (1)
  Haitao Yu (1)
  Huiyan Li (2)
  
  1. School of Electrical Engineering and Automation ; Tianjin University ; Tianjin ; 300072 ; People鈥檚 Republic of China
  2. School of Automation and Electrical Engineering ; Tianjin University of Technology and Education ; Tianjin ; 300222 ; People鈥檚 Republic of China
  
• 关键词：UKF ; Iterative learning control ; Epilepsy ; Neural mass model
• 刊名：Cognitive Neurodynamics
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：9
• 期：1
• 页码：31-40
• 全文大小：853 KB
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• 刊物主题：Biomedicine general; Neurosciences; Computer Science, general; Artificial Intelligence (incl. Robotics); Biochemistry, general; Cognitive Psychology;
• 出版者：Springer Netherlands
• ISSN：1871-4099

文摘

A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment.