A Brain-Computer Interface (BCI) is a unidirectional communication system used to input predefined control commands to an external apparatus without using conventional motor output pathways but only monitoring the brain activity. A typical BCI system is composed of three main subsystems: (i) a brain acquisition system, usually Electroencephalography (EEG) or Magnetoencephalography (MEG); (ii) a signal processing system that includes pre-processing of the EEG/MEG signals, feature extraction and the translation of the features in commands by using a machine learning (ML) classification stage; (iii) a computer controlled by the generated commands providing a visual feedback. In this thesis we focus on a specific family of BCI that leverage on EEG Source Imaging (ESI) to map the EEG recording to intracranial currents by using electromagnetic models of the brain.