The aim of the course is to provide a series of skills to perform advanced analysis of high-density electroencephalographic (hdEEG) data. The course covers all the steps required to perform functional connectivity analysis of resting state hdEEG data. In particular, the different steps include EEG signal preprocessing, head modelling, EEG source localization, and connectivity analysis. Connectivity analysis include two different approaches to extract brain networks: i) data-driven, based on independent component analysis (ICA) and ii) hypothesis-driven, based on the definition of specific regions of interest (seed). All lessons will include both theoretical and practical sessions, these latter to allow familiarizing with NET software. During the lessons, students' direct interaction is highly encouraged.
- Docente: Marco Marino