A Robust Nonlinear Observer for a Class of Neural Mass Models

A new method of designing a robust nonlinear observer is presented for a class of neural mass models by using the Lur'e system theory and the projection lemma. The observer is robust towards input uncertainty and measurement noise. It is applied to estimate the unmeasured membrane potential of...

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Detalles Bibliográficos
Autores principales: Liu, Xian, Miao, Dongkai, Gao, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3981059/
https://www.ncbi.nlm.nih.gov/pubmed/24790554
http://dx.doi.org/10.1155/2014/215943
Descripción
Sumario:A new method of designing a robust nonlinear observer is presented for a class of neural mass models by using the Lur'e system theory and the projection lemma. The observer is robust towards input uncertainty and measurement noise. It is applied to estimate the unmeasured membrane potential of neural populations from the electroencephalogram (EEG) produced by the neural mass models. An illustrative example shows the effectiveness of the proposed method.

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