Simulation of ictal EEG with a neuronal population model

In order to analyze the behavior of EEG and its neural physiological mechanism, a neuronal population model has been adopted to simulate ictal EEG signals, and the modeling performance has been analyzed in this work. A delay unit and a gain unit were added to Wendling model to fit EEG signals in tim...

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Bibliographic Details
Main Authors: Zhen Ma, Weidong Zhou, Qi Yuan, Shujuan Geng
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Computational modeling
Delay
Educational institutions
Electroencephalography
Fitting
Genetic algorithms
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Summary:In order to analyze the behavior of EEG and its neural physiological mechanism, a neuronal population model has been adopted to simulate ictal EEG signals, and the modeling performance has been analyzed in this work. A delay unit and a gain unit were added to Wendling model to fit EEG signals in time domain, and genetic algorithm was used to identify an optimal set including of five parameters to minimize the error between real EEG and simulated EEG. The results show that the model can produce an approximation of the real EEG signal well.
DOI:10.1109/ISBB.2011.6107656