A Brain-Computer Interface (BCI) uses electrophysiological measures of brain function to enable individuals to communicate with the external world, bypassing normal neuromuscular pathways. While it has been suggested that this control can be applied for neuroprostheses, few studies have demonstrated practical BCI control of a prosthetic device. In this paper, an electroencephalogram (EEG)-based motor imagery BCI is presented to control movement of a prosthetic hand. The hand was instrumented with force and angle sensors to provide haptic feedback and local machine control. Using this system, subjects demonstrated the ability to control the prosthetic's grasping force with accuracy comparable to an EMG-based control scheme. Further work is necessary to improve the integration of BCI control strategies with prostheses.