The brain is the most complex organ of the human body. In order to understand its functioning, advanced brain imaging techniques, such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and electroencephalography (EEG) have been developed in the last decades. These brain imaging techniques allow us to non-invasively measure brain activity at different spatial and temporal scales, giving us insight into the neuronal mechanisms associated with behavior. An important area of research is the one that focuses on brain dynamics in the so-called resting state, rather than during the execution of explicit task sets. In an idling condition, the brain is thought to prepare itself for future demands by generating coordinated dynamics that largely overlap with patterns of previous activity. These coordinates dynamics can be studied by using functional connectivity methods. If different areas of the brain are functionally connected, these are said to form a brain network. Conversely, areas of the brain that are only weakly connected, are considered to be part of different brain networks. Accordingly, functional connectivity analyses permit to investigate the functional architecture of the human brain. Functional connectivity is typically measured using fMRI data, which are spatially very accurate but are sampled only every few seconds, and reflect hemodynamic processes that are only indirectly linked to neuronal activity. On the other hand, few studies have alternatively used MEG for brain network imaging. MEG has very high temporal resolution and can provide measures of neural activity, but its applications are limited by the fact that it as it has very high requirements in terms of necessary infrastructure and maintenance. No EEG study has ever been successful in the imaging of brain network. However, recent developments in the field of EEG, particularly the introduction of the high-density EEG (hdEEG) technology, open new perspectives. One of the most important limiting factors for the use of hdEEG as brain imaging tool remains its spatial resolution. The main goal of this work is to develop the necessary methodological approaches that are needed to improve the spatial resolution of hdEEG, and to permit brain network imaging using this technique. In particular, we will develop tools for signal preprocessing, head modelling, brain activity reconstruction and connectivity analysis. As for the hdEEG connectivity tools, we will implement both data-driven and hypothesis-driven analysis strategies, as those used in fMRI and MEG connectivity studies. The developed techniques will be extensively validated, and the potential advantages brought by the use of hdEEG connectivity as compared to fMRI connectivity will be demonstrated. Although the proposed methods were applied to resting data, the same approach can be used for task-related data. We believe that our analysis tools for hdEEG can find several applications in the field of brain imaging and neuroscience.