Novel signal processing methods enlarged the spectrum of application of electroencephalography (EEG), making it a neuroimaging tool helping the investigations of cortical dynamics at the whole-brain scales. The emergence of network neuroscience translated the math-derived network study methods to the investigation of brain organization. The possibility of using EEG as a neuroimaging tool resulted into novel research approaches for the study of the neural alterations characterizing clinical conditions. Moreover, the introduction of artificial intelligence in clinical settings allowed the usage of EEG-derived signals to be introduced in an automatic learning workflow. This results in the human–machine interaction (i.e., brain–computer interfaces, BCIs) applied in the context of neurorehabilitation. In the present chapter, we aimed at providing a global perspective of the evolution of EEG application from brain network investigation to BCI-related rehabilitation, finally arriving at the general framework of human–machine interaction.