At six months after brain injury, about 65% of stroke survivors have been shown to be unable to incorporate the affected hand into activities of daily living (ADL). Using a reliable Brain-Machine-Interface (BMI) together with Neural Electronic Stimulation (NES) is a possible solution for the restoration of hand function in severely impaired hemiparetic stroke survivors. However, discoordination, i.e. the abnormal coupling between adjacent joints, causes an expected reduction in the performance of BMI algorithms. In this study, we test whether the active support of an ACT3D robot can increase the performance of two brain-machine-interface (BMI) algorithms in separating the subject's intention to open or close the impaired hand during reach. Improvement in recognition rate was obtained in 4 chronic hemiparetic stroke subjects when support from the robot was available. Further analysis on one subject suggests that such an improvement is related to quantitative changes in cortical activity. This result suggests that the ACT3D robot can be used to train severely impaired stroke subjects to use a BMI-controlled NES device.