A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation

Abstract

This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To address this, we propose a framework for generating synthetic EEG signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (GC). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for GC analysis. The findings could guide the development of more accurate dynamic connectivity techniques.