Abstract
The neural mass models have been widely used for simulating the highly complex Electroencephalogram (EEG) rhythmic activity, when the extrinsic input p(t) passes through the model, similar oscillatory signals are produced. In this paper, we present an empirical exploration to the theoretical prediction of such a model by fitting the actual EEG signal to the Jansen’s neural mass model. The results suggest that the model can produce good approximation to the actual EEG signal. The extrinsic input used formerly has a relatively big SD (standard deviation), which may produce unreliable synthetic data, even bias the analysis results. In our study, the mean values of estimated p(t) fall well within the interval for the simulate study recommended by previous reports, but the SD of p(t) is far less than the experience value used before.
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Fang, X., Hu, Z., Shi, P. (2010). Neural Mass Model Driven Nonlinear EEG Analysis. In: Liao, H., Edwards, P.J."., Pan, X., Fan, Y., Yang, GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15699-1_48
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DOI: https://doi.org/10.1007/978-3-642-15699-1_48
Publisher Name: Springer, Berlin, Heidelberg
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