Closed-Loop Proportion-Derivative Control of Suppressing Seizures in a Neural Mass Model

A closed-loop neuromodulation automatically adjusts stimuli according to brain response in real time. It is viewed as a promising way to control medically intractable epilepsy. A suitable closed-loop modulation strategy, which is robust enough to unknown nonlinearities, dynamics, and disturbances, is in great need in the clinic. For the specialization of epilepsy, the Jansen’s neural mass model is utilized to simulate the undesired high amplitudes epileptic activities, and active disturbance rejection control is designed to suppress the high amplitudes of epileptiform discharges. With the help of active disturbance rejection control, closed-loop roots of the system are far from the imaginary axis. Time domain response shows that active disturbance rejection control is able to control seizure no matter whether disturbances exist or not. At the same time, frequency domain response presents that enough stability margins and a broader range of tunable controller parameters can be obtained. Stable regions have also been presented to provide guidance to choose the parameters of active disturbance rejection control. Numerical results show that, compared with proportional-integral control, more accurate modulation with less energy can be achieved by active disturbance rejection control. It confirms that the active disturbance rejection control-based neuromodulation solution is able to achieve a desired performance. It is a promising closed-loop neuromodulation strategy in seizure control.

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Mean-Field Models for EEG/MEG: From Oscillations to Waves

Brain Topography ◽

10.1007/s10548-021-00842-4 ◽

2021 ◽

Author(s):

Áine Byrne ◽

James Ross ◽

Rachel Nicks ◽

Stephen Coombes

Keyword(s):

Gap Junction ◽

Large Scale ◽

Mean Field ◽

Coarse Grained ◽

Neural Mass Model ◽

Neuron Network ◽

Mass Model ◽

Mean Field Model ◽

Neural Mass ◽

The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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Detecting Epileptic Seizures Based on Parameter Estimation of a Neural Mass Model

International Journal of Psychophysiology ◽

10.1016/j.ijpsycho.2021.07.325 ◽

2021 ◽

Vol 168 ◽

pp. S109

Author(s):

Junfeng Lu ◽

Hong Wan ◽

Rui Zhang ◽

Mingming Chen

Keyword(s):

Parameter Estimation ◽

Epileptic Seizures ◽

Neural Mass Model ◽

Mass Model ◽

Neural Mass

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Principal dynamic mode analysis of neural mass model for the identification of epileptic states

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.4967734 ◽

2016 ◽

Vol 26 (11) ◽

pp. 113118 ◽

Cited By ~ 1

Author(s):

Yuzhen Cao ◽

Liu Jin ◽

Fei Su ◽

Jiang Wang ◽

Bin Deng

Keyword(s):

Mode Analysis ◽

Neural Mass Model ◽

Dynamic Mode ◽

Mass Model ◽

Neural Mass

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Neural Mass Model

Encyclopedia of Computational Neuroscience ◽

10.1007/978-1-4614-6675-8_100390 ◽

2015 ◽

pp. 1898-1898

Keyword(s):

Neural Mass Model ◽

Mass Model ◽

Neural Mass

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Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

Indian Journal of Signal Processing ◽

10.54105/ijsp.c1008.081321 ◽

2021 ◽

pp. 1-7

Author(s):

Sheikh Md. Rabiul Islam ◽

◽

Md. Shakibul Islam ◽

Keyword(s):

Signal Generation ◽

Neural Mass Model ◽

Pathophysiological Mechanism ◽

Eeg Signal ◽

Eeg Signals ◽

Mass Model ◽

Detailed Model ◽

Simulation Based ◽

Neural Mass ◽

The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

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