Computational models in Electroencephalography

TL;DR

This review discusses the current state of computational modeling in EEG, highlighting its role in understanding brain activity mechanisms, designing experiments, and advancing clinical applications.

Contribution

It clarifies the diverse definitions of computational models in EEG and outlines how these models integrate electrophysiology, network dynamics, and behavior.

Findings

Computational models help investigate EEG signal generation mechanisms.

Models are used to design and test hypotheses in silico.

Understanding EEG mechanisms aids clinical application development.

Abstract

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses \textit{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 \emph{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.