A Brain Computer Interface (BCI) can be defined as any system that can track the person’s intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require to set up the appropriate experimental paradigm while recording the brain signal and to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named Functional Source Separation (FSS). As it comes from the Blind Source Separation algorithms, it exploits the most rich information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as optimization procedure allowing to exploit functional constraints non differentiable. Moreover a minor section is included. Information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behavior in these patients is provided. Relevant BCI features - spatial and time-frequency properties - are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigated the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. The review provides a description of the FSS technique, a promising tool for BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients.