A brain machine interface (BMI) is a system that directly decodes the user's intention from brain signals to control a robot or an external device or stimulator. These systems have shown to be potential tools for the rehabilitation of motor-impaired patients after stroke. Brain signals can be measured with non-invasive methods such as electroncephalography (EEG) or with higher resolution techniques like intracortical neural recordings. On the other hand, electromyographic (EMG) activity from the muscles has been widely utilized to control prostheses, rehabilitation robots or electrical stimulators, as it provides a more direct and robust measurement of the user's motion intention than non-invasive brain signals. However, EMG also has some limitations and some stroke patients might not have residual EMG activity. As a result, hybrid BMIs (hBMIs) combining brain and muscle signals to control a rehabilitation or assistive device have been recently proposed as an improved and more robust technology than only-brain or only-muscle based interfaces. We have developed a novel hBMI based on EEG and EMG that aims at eliciting rehabilitation by acting both at the brain and muscle levels, which are both compromised after stroke. We have tested it in a preliminary study with stroke patients showing encouraging results (Sarasola-Sanz, 2017) and are currently working on its testing in a larger population as well as on more accurate invasive hBMI systems.