From EEG Source Localization to Brain Network Analysis: A Computational Neuroscience Perspective

Electroencephalography (EEG) source localization has evolved from a technical solution to an inverse problem and into a fundamental framework for investigating large-scale brain networks. By reconstructing cortical activity from scalp-recorded signals, source-space EEG improves anatomical interpretability and reduces confounding effects of volume conduction compared with sensor-level analyses. These advances have enabled more reliable estimation of functional and effective connectivity, graph-theoretical characterization of brain networks, and the integration of machine learning for automated analysis and clinical decision support. Nevertheless, the accuracy and reproducibility of source-level analyses remain highly dependent on forward modeling, inverse algorithms, preprocessing strategies, and connectivity estimation methods. Recent methodological developments have further expanded the role of EEG source imaging in computational neuroscience, supporting applications ranging from network-based investigations of brain function to biomarker discovery and disease classification. This review summarizes the computational framework underlying source-level EEG, beginning with the forward and inverse problems and progressing through source reconstruction, connectivity analysis, graph-theoretical network modeling, and emerging machine learning and deep learning techniques. We also highlight current clinical applications and major methodological challenges, including source leakage, model uncertainty, reproducibility, and interpretability, and outline future directions for robust and clinically translatable source-level EEG analysis.