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Stroke is a cardiovascular accident within the brain resulting in motor and sensory impairment in most of the survivors. A stroke can produce complete paralysis of the limb although sensory abilities are normally preserved. Functional electrical stimulation (FES), robotics and brain computer interfaces (BCIs) have been used to induce motor rehabilitation. In this work we measured the brain activity of healthy volunteers using electroencephalography (EEG) during FES, passive movements, active movements, motor imagery of the hand and resting to compare afferent and efferent brain signals produced during these motor related activities and to define possible features for an online FES-BCI. In the conditions in which the hand was moved we limited the movement range in order to control the afferent flow. Although we observed that there is a subject dependent frequency and spatial distribution of efferent and afferent signals, common patterns between conditions and subjects were present mainly in the low beta frequency range. When averaging all the subjects together the most significant frequency bin comparing each condition versus rest was exactly the same for all conditions but motor imagery. These results suggest that to implement an on-line FES-BCI, afferent brain signals resulting from FES have to be filtered and time-frequency-spatial features need to be used.