Fuzzy adaptive unscented Kalman filter control of epileptiform spikes in a class of neural mass models

A new closed-loop control method based on the fuzzy adaptive unscented Kalman filter (FAUKF) is proposed to suppress epileptiform spikes in a class of neural mass models with uncertain measurement noise. The FAUKF is used to estimate the nonlinear system states of the underlying models and amend measurement noise adaptively. The control law is constructed via the estimated states. Numerical simulations illustrate the efficiency of the proposed method.

[1]  Han Ho Choi,et al.  Fuzzy speed control with an acceleration observer for a permanent magnet synchronous motor , 2012 .

[2]  Qing Gao,et al.  Control of epileptiform spikes based on nonlinear unscented Kalman filter , 2014 .

[3]  Jamshaid Ali,et al.  Performance comparison among some nonlinear filters for a low cost SINS/GPS integrated solution , 2010 .

[4]  Alim Louis Benabid,et al.  What the future holds for deep brain stimulation , 2007, Expert review of medical devices.

[5]  Dah-Jing Jwo,et al.  Performance enhancement for ultra-tight GPS/INS integration using a fuzzy adaptive strong tracking unscented Kalman filter , 2013 .

[6]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[7]  Chen Ming,et al.  Application of Adaptive Kalman Filtering Based on Fuzzy Logic to theIntegrated GPS/INS Navigation , 2006 .

[8]  Fabrice Wendling,et al.  Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals , 2000, Biological Cybernetics.

[9]  Kaspar Anton Schindler,et al.  Self-organised transients in a neural mass model of epileptogenic tissue dynamics , 2012, NeuroImage.

[10]  L. Ding,et al.  Stabilizing control of Hopf bifurcation in the Hodgkin–Huxley model via washout filter with linear control term , 2010 .

[11]  Sridhar Sunderam,et al.  Toward Rational Design of Electrical Stimulation Strategies for Epilepsy Control , 2022 .

[12]  Jianbo Gao,et al.  Revised Papers , 2022 .

[13]  Stefano Mariani,et al.  Unscented Kalman filtering for nonlinear structural dynamics , 2007 .

[14]  A. Gholami,et al.  A new adaptive fuzzy sliding mode observer for a class of MIMO nonlinear systems , 2012 .

[15]  M. Koubeissi,et al.  Cerebellar and thalamic stimulation treatment for epilepsy. , 2007, Acta neurochirurgica. Supplement.

[16]  Kaspar Anton Schindler,et al.  Intermittent spike–wave dynamics in a heterogeneous, spatially extended neural mass model , 2011, NeuroImage.

[17]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[18]  Wei Zhang,et al.  Chaotic synchronization and secure communication based on descriptor observer , 2009 .

[19]  Dragan Nesic,et al.  A robust circle criterion observer with application to neural mass models , 2012, Autom..

[20]  Xian Liu,et al.  Parameter estimation and control for a neural mass model based on the unscented Kalman filter. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Steven J Schiff,et al.  Kalman filter control of a model of spatiotemporal cortical dynamics , 2008, BMC Neuroscience.

[22]  Zhao Yude.  A Kind of Fuzzy Adaptive Kalman Filter and Its Application on INS/Doppler Integrated Navigation , 2011 .

[23]  Xian Liu,et al.  Fuzzy PID control of epileptiform spikes in a neural mass model , 2013 .

[24]  Frank Marten,et al.  Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution , 2012, NeuroImage.

[25]  J. Villemure,et al.  Rationale, mechanisms of efficacy, anatomical targets and future prospects of electrical deep brain stimulation for epilepsy. , 2007, Acta neurochirurgica. Supplement.