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

Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Li, Huiyan

doi:10.1007/s11571-014-9306-0

Cited by 22 publications

(7 citation statements)

References 51 publications

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“…The closed-loop electrophysiology system has the potential to provide a theoretical basis for choosing stimulus patterns for neural activity modulation. Proportionalintegral (PI) control, fuzzy control, predictive control, and other algorithms have been successfully applied to obtain the optimal stimulus signal [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Hardware Experiment Platform for Closed-Loop Electrophysiology

Liu

Chang

Wang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Targeted stimulation of nervous system has become an increasingly important research tool as well as therapeutic modality, and the stimulation signal acquisition based on the expected signal needs a closed-loop system. Due to the difficulty of biological experiments, the real-time simulation of neural activity is of great significance for the mechanism analysis and the performance improvement of neuromodulation techniques. This paper proposes a real-time hardware experimental platform for closed-loop electrophysiology. The platform integrates a neural computing module and a real-time control module on TMS320F28377D digital signal processors (DSP), and it reserves a programmable interface for users to call the required modules and set module parameters simultaneously. The platform has high compatibility and can be used for closed-loop electrophysiological experiments with different models, different control algorithms and different clamps. We implemented the thalamocortical relay neural computing model and iteration improved proportionalintegral algorithm on the platform for experimental verification in this paper. The neuron firing waveforms of the DSP platform and the MATLAB R2020b simulation waveforms are consistent. Under the same physiological time, the simulation speed of DSP platform is 3 times faster than that of the Intel Core i5-8400 CPU computer, and the neural firing rate of DSP platform is due to the real-time. This platform can be used as a tool to explore the working mechanism of the nervous system. It may promote the development of neuroscience, especially the field of closed-loop neuroscience.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Hardware Experiment Platform for Closed-Loop Electrophysiology

Liu

Chang

Wang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…So far, the closed-loop neuro-modulation has become a trend in the treatment of epilepsy [13], and numerous efforts have been made on such topic, such as PID control [4], [14], feedback linearization control [15], fuzzy PID control [16], closed-loop iterative learning control (ILC) based on unscented Kalman filter (UKF) [17] and parameter estimation and control based on particle swarm optimization (PSO) [18]. Above closed-loop neuro-modulation algorithms are effective to some extent.…”

Section: Introductionmentioning

confidence: 99%

Seizure Control by a Learning Type Active Disturbance Rejection Approach

Wei

Xia

et al. 2019

IEEE Access

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Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection control (ADRC) is utilized for its satisfied disturbance rejection and regulation performance. However, fixed observer parameters are difficult to fit the time-varying electrophysiological signals. Therefore, based on the estimation errors, an iterative learning approach is designed to get the parameters of an extended state observer (ESO). By combining the advantages of ADRC and the iterative learning, a learning type ADRC (LTADRC) is proposed to suppress the high amplitude epileptiform waves generated by the Jansen's neural mass model (NMM). For those variable parameters of an ESO, scalable bandwidths can be obtained to adapt to time-varying disturbance signals. It is of great significance for both ADRC and the neuro-modulation of epilepsy. Simulation results show that, compared with ADRC, much better performance can be obtained. It may provide a promising closed-loop regulation way for epilepsy in clinics. INDEX TERMS ADRC, a learning type ADRC, epilepsy, NMM, closed-loop modulation.

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“…For instance, in C ¸etin and Beyhan (2018), an adaptive unscented Kalman filter-based optimal controller is proposed to control the dynamics of uncertain cortex with a single membrane potential measurement in their recent study. In Shan et al (2015), to reproduce the dynamics and to estimate the unmeasurable parameters of the model, a control framework has been proposed to inhibit epilepticform wave in a neural mass model by external electric field. The values of neurophysiological parameters were estimated using the detailed biophysical model of brain activity in Rowe et al (2004).…”

Section: Introductionmentioning

confidence: 99%

Model-based robust suppression of epileptic seizures without sensory measurements

Çetin

2019

Cogn Neurodyn

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Uncontrolled seizures may lead to irreversible damages in the brain and various limitations in the patient's life. There exist experimental studies to stabilize the patient seizures. However, the experimental setups have many sensory devices to measure the dynamics of the brain cortex. These equipments prevent to produce small portable stabilizers for patients in everyday life. Recently, a comprehensive cortex model is introduced to apply model-based observers and controllers. However, this cortex model can be uncertain and have time-varying parameters. Therefore, in this paper, a robust Takagi-Sugeno (TS) controller and observer are designed to suppress the epileptic seizures without sensory measurements. The unavailable sensory measurements are provided by the designed nonlinear observer. The exponential convergence of the observer and controller is satisfied by the feedback parameter design using linear matrix inequalities. In addition, TS fuzzy observer-controller design has been compared with the conventional PID method in terms of control performance and design problem. The numerical computations show that the epileptic seizures are more effectively suppressed by the TS fuzzy observer-based controller under uncertain membrane potential dynamics.

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

Cited by 22 publications

References 51 publications

A Real-Time Hardware Experiment Platform for Closed-Loop Electrophysiology

A Real-Time Hardware Experiment Platform for Closed-Loop Electrophysiology

Seizure Control by a Learning Type Active Disturbance Rejection Approach

Model-based robust suppression of epileptic seizures without sensory measurements

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