A parameter estimation method for neural mass model based on the improved chimp optimization algorithm and Riemannian geometry

Neural mass model (NMM) serves as an effective tool for understanding and exploring the complex dynamics of brain systems. Accurately estimating the model parameters of NMM is highly important for building brain models driven by observed electroencephalogram (EEG) data. However, existing methods for...

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Veröffentlicht in:	Chaos, solitons and fractals Jg. 194; S. 116219
Hauptverfasser:	Yan, Shaoting, Shi, Xiaochu, Li, Ruiqi, Zhang, Lipeng, Zhang, Rui, Chen, Mingming, Li, Meng, Zhang, Hui, Li, Runtao, Shi, Li, Hu, Yuxia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.05.2025
Schlagworte:	Chimp optimization algorithm Disorder of consciousness Electroencephalogram Neural mass model Parameter estimation Riemannian geometry Riemannian geometry Parameter estimation Chimp optimization algorithm Neural mass model Electroencephalogram Disorder of consciousness
ISSN:	0960-0779
Online-Zugang:	Volltext
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