Project start: 1.10.2025.

Facing persistent challenges in understanding the complex functions of the brain and developing effective diagnostic and therapeutic tools for neurological disorders, this research proposes a detailed analysis of brain functional connectivity measures derived from EEG signals. This study focuses on a detailed analysis of the use of undirected and directed static brain functional connectivity measures in EEG signal analysis. The aim of the research is to examine how different functional connectivity measures can contribute to a better understanding of brain activity dynamics, with a particular emphasis on distinguishing direct and indirect interactions between brain regions. Undirected functional connectivity is based on analyses that measure coherence or correlation (and similar ones) between regions without considering the direction of information, while directed connectivity includes methods such as Granger causality and transfer entropy (and similar ones), which explore the direction of information between regions, allowing for a deeper understanding of causal relationships between them. The use of complex Pearson correlation coefficients and other relevant measures will help in distinguishing connections with and without the influence of volume conduction, providing a more accurate insight into the actual interactions between regions. Furthermore, the research will encompass dynamic functional connectivity, analyzed using techniques for tracking temporal changes, enabling a more precise assessment of changes in brain region connectivity. These measures and methods will be tested on synthetic and real EEG signals, ensuring a broad framework for their potential applications in neuroscience, with the ultimate goal of improving our understanding of brain function and facilitating the development of innovative diagnostic and therapeutic approaches.