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

2016 ◽  
Vol 26 (11) ◽  
pp. 113118 ◽  
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

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

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.

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

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

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

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.

Delayed Feedback-Based Suppression of Pathological Oscillations in a Neural Mass Model

2019 ◽  
pp. 1-11
Author(s):  
Chen Liu ◽  
Changsong Zhou ◽  
Jiang Wang ◽  
Chris Fietkiewicz ◽  
Kenneth A. Loparo
Keyword(s):  
Delayed Feedback ◽  
Neural Mass Model ◽  
Mass Model ◽  
Neural Mass

scholarly journals A Stochastic Version of the Jansen and Rit Neural Mass Model: Analysis and Numerics

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Markus Ableidinger ◽  
Evelyn Buckwar ◽  
Harald Hinterleitner
Keyword(s):  
Model Analysis ◽  
Neural Mass Model ◽  
Mass Model ◽  
Stochastic Version ◽  
Neural Mass

scholarly journals Periodically forced neural mass model: entrainment and complex behavior

2010 ◽  
Vol 11 (S1) ◽  
Author(s):  
Andreas Spiegler ◽  
Thomas R Knösche ◽  
Fatihcan M Atay
Keyword(s):  
Neural Mass Model ◽  
Complex Behavior ◽  
Mass Model ◽  
Neural Mass
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