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Computational Models in Electroencephalography

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Glomb Katharina, Cabral Joana, Cattani Anna, Mazzoni Alberto, Raj Ashish, Franceschiello Benedetta,
Project Exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging : application to physiological and epileptic networks
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Original article (peer-reviewed)

Journal Brain Topography
Page(s) 1
Title of proceedings Brain Topography
DOI 10.1007/s10548-021-00828-2

Open Access

URL http://doi.org/10.1007/s10548-021-00828-2
Type of Open Access Publisher (Gold Open Access)

Abstract

AbstractComputational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.
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