Computational 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.

计算模型位于基础神经科学和医疗保健应用的交叉点，因为它们允许研究人员在计算机上测试假设并预测在现实中很难测试的实验和交互的结果。然而，神经科学和心理学不同领域的研究人员以许多不同的方式理解“计算模型”的含义，阻碍了交流和协作。在这篇综述中，我们指出了脑电图 (EEG) 中计算建模的最新技术，并概述了如何使用这些模型来整合来自电生理学、网络级模型和行为的发现。一方面，计算模型用于研究产生大脑活动的机制，例如用脑电图测量，例如在不同频带和/或不同空间拓扑下瞬时出现的振荡。另一方面，计算模型用于设计实验和在计算机上测试假设。EEG 计算模型的最终目的是全面了解 EEG 信号背后的机制。这对于准确解释 EEG 测量结果至关重要，最终可能用于开发新的临床应用。