Design and Development of an Intuitive Software Tool for Automated Detection and Analysis of Single-Pulse Electrical Stimulation in Raw iEEG Signals: Enhancing Understanding of Cortico-Cortical Potentials

This thesis presents the development of an innovative software tool designed for the automated detection and analysis of cortico-cortical evoked potentials (CCEPs) from raw intracranial electroencephalography (iEEG) signals. CCEPs are crucial for exploring the functional connectivity between cortical areas, thus enhancing our understanding of the brain's communication pathways. The software enables efficient analysis of CCEPs, which is instrumental in advancing neurological research and improving clinical outcomes in neurosurgery. The thesis is structured into several key sections: an introduction to the study's aims and significance; a comprehensive literature review split into discussions on the structural and functional connectome, and cortico-cortical potentials; a detailed exposition of the software's design, including its user interface and functionalities for channel selection, CCEP analysis, and data handling; and a conclusion that not only summarizes the findings but also outlines future research directions. Future enhancements of the software include integration with visualization tools for better spatial understanding of electrode locations, expansion to high-frequency stimulation analysis, and broader applications in brain mapping critical regions like speech areas. These enhancements aim to refine the tool's capability in mapping functional brain connectivity, thereby supporting more precise neurosurgical planning and potentially improving intervention strategies in various neurological conditions. This thesis contributes to the cognitive sciences by merging technical innovation with practical applications, providing a robust platform for further advancements in the understanding of brain connectivity.