A novel observer design method for neural mass models

Neural mass models can simulate the generation of electroencephalography（EEG） signals with different rhythms,and therefore the observation of the states of these models plays a significant role in brain research. The structure of neural mass models is special in that they can be expressed as Lurie s...

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Bibliographic Details
Published inChinese physics B Vol. 24; no. 9; pp. 68 - 72
Main Author 刘仙 苗东凯 高庆 徐式蕴
Format Journal Article
LanguageEnglish
Published 01.09.2015
Subjects
Brain
Computer simulation
Convergence
Design engineering
Dynamical systems
Electroencephalography
Errors
Lurie型
Lurie系统
Mathematical models
数值模拟
神经网络观测器
系统理论
设计方法
质量模型
非线性系统
Online AccessGet full text
ISSN1674-1056
2058-3834
1741-4199
DOI10.1088/1674-1056/24/9/090207

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Summary:Neural mass models can simulate the generation of electroencephalography（EEG） signals with different rhythms,and therefore the observation of the states of these models plays a significant role in brain research. The structure of neural mass models is special in that they can be expressed as Lurie systems. The developed techniques in Lurie system theory are applicable to these models. We here provide a new observer design method for neural mass models by transforming these models and the corresponding error systems into nonlinear systems with Lurie form. The purpose is to establish appropriate conditions which ensure the convergence of the estimation error. The effectiveness of the proposed method is illustrated by numerical simulations.
Bibliography:Neural mass models can simulate the generation of electroencephalography（EEG） signals with different rhythms,and therefore the observation of the states of these models plays a significant role in brain research. The structure of neural mass models is special in that they can be expressed as Lurie systems. The developed techniques in Lurie system theory are applicable to these models. We here provide a new observer design method for neural mass models by transforming these models and the corresponding error systems into nonlinear systems with Lurie form. The purpose is to establish appropriate conditions which ensure the convergence of the estimation error. The effectiveness of the proposed method is illustrated by numerical simulations.
observer design,neural mass model,Lurie system theory
11-5639/O4
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/24/9/090207