[An outlook on the present and future of brain-machine interface research]

The goal of brain-machine interface (BMI) research is to interpret brain signals in order to control an external device. Substantial progress toward this goal has been achieved over the last decade. Currently, BMI algorithms can translate neural signals into motor commands that reproduce arm-reaching and hand-grasping movements in artificial actuators, thereby promising the restoration of limb mobility in paralyzed people. In one study, a tetraplegic human subject used a clinical neuromotor prosthesis to restore his communication and mobility. Furthermore, a recently developed neural decoding technology provides an effective means to read out mental states from human brain activity. Decoding of mental states could be used for direct human-human communication outside the brain's normal pathways. However, for BMI practical, long-term stability of signal interpretation is required. Unfortunately, the classical invasive BMI methods suffer from poor long-term stability because of deterioration in signal quality. Two new approaches to long-term BMI applications are showing promising results in maintaining signal quality. One is the use of newly developed electrodes that are less harmful to neural tissues, and the other is the use of electrocorticograms (ECoGs), which measure population activity of neurons with electrodes placed on the surface of the brain. Both these new technologies facilitate clearer signals from the brain and greater stability of brain signals over time. In this review, we summarize the previous BMI approaches and shed light upon the new advances that may enable long-term BMI use.