Brain-machine interface research Mikhail AL et al. to not only replicate basic gait and stepping functions, but also adapt to postural control. In addition to developing a BMI for bipedal locomotion, we have developed a proof of concept BMI for postural control.25 Using this experimental setup, in which monkeys stood on a platform that produced abrupt horizontal movements and evoked postural pertur- bations, we demonstrated recently that cortical neurons modulate their firing rate in relation to changes in posture. We then applied BMI decoders to extract information about these changes from cortical ensemble activity. The platform was driven either rhythmically, allowing the animal to anticipate the upcoming perturbation, or with a random time delay between movements so that the animal was unable to predict when the next movement would come. Similarly to our treadmill experiments, single unit analysis of the neural activity showed that a vast majority of cortical neurons (mostly in M1 and S1 representations of the legs) were highly modulated in association with compensatory postural reactions. These modulations were directionally tuned: neuronal rates changed differently depending on the direction of platform displacement. The BMI decoder based on a linear model predicted postural disturbances with good signal-to-noise ratios. Importantly, the decoder yielded different results depending on anticipated versus unanticipated platform displacements. Model parameters were different in these two cases, and anticipated displace- ments were predicted with higher accuracy than antici- pated. These results show the feasibility of a BMI for restoration of upright posture. The concrete implementation of such a BMI should be based on shared control rather than relying solely on cortical modulations for postural control. INTEGRATING FUNCTIONAL ELECTRICAL STIMULATION (FES) TO THE BMI PARADIGM The issue of directly actuating the movements of paralyzed legs has been addressed in many clinical studies in spinal cord injury patients. Robotic orthoses73–75 and functional electrical stimulation (FES) devices76–79 have been introduced as therapies for leg paralysis. FES devices for the lower extremities date back to the 1960s when the first FES application to restore standing was developed.80 In this study, paraplegic subjects were able to stand after FES was applied to their quadriceps and gluteus maximus muscles. Also in the 1960s, FES of the common peroneal nerve was used to correct foot drop during the swing phase of the gait.81 Foot drop stimulators have been commercialized.82,83 This technique is used in a ParastepH system developed by Sigmedics, Inc, Fairborn, OH, USA.84,85 At present, a variety of FES methods have been developed that allow stimulation of multiple muscle groups with surface or intramuscular electrodes. Importantly, FES systems have many positive therapeutic effects on patients with incomplete spinal cord injury (SCI).79 Unfortunately, current FES systems allow for only crude movement such as short-distance ambulation or short-term standing, require support from a walker or similar tool, and are primarily offered as a way to avoid medical complications of prolonged limb inactivity rather than as an alternative to locomotion restoration. Addi- tionally, such systems require manual actuation by the user, resulting in a limited and arguably non-intuitive way to restore leg function. Building on our success in BMIs that enact bipedal locomotion and postural control, we are currently developing 30 CLINICS 2011;66(S1):25-32 a cortically driven FES system for the restoration of walking. This novel BMI will be tested in a monkey model of bipedal walking. Our objective is to build a BMI-driven FES system that produces bipedal locomotion patterns by converting cortical ensemble activity into stimulation patterns that drive leg muscles. We suggest that BMI control over bipedal locomotion can be established by recording large-scale cortical activity from the sensorimotor cortex, extracting locomotion patterns from the raw cortical signals and converting them into trains of FES applied to multiple leg muscles. WHOLE-BODY NEUROPROSTHETIC As follows from our results on BMIs that enact leg movements, BMIs for the whole body are likely to become a real possibility in the near future. We propose the develop- ment of a whole-body BMI in which neuronal ensemble activity recorded from multiple cortical areas in rhesus monkeys controls the actuators that enact movements of both upper and lower extremities.37 This BMI will be first implemented in a virtual environment (monkey avatar) and then using a whole-body exoskeleton. In these experiments we will also examine the plasticity of neuronal ensemble properties caused by their involvement in the whole-body BMI control and the ability of cortical ensembles to adapt to represent novel external actuators. Furthermore, we will also explore the ability of an animal to navigate a virtual environment using both physical and neural control that engages both the upper and lower limbs. The first phase of these experiments will be to train the animals to walk in a bipedal manner on a treadmill while assisting the navigation with a hand steering control. We have already built a virtual environment needed for the monkey to navigate using 3D visualization software. Within this environment, the monkey’s body is represented by a life-like avatar. This representation is viewed in the third person by the monkey and employs real-world inverse kinematics to move, allowing the avatar’s limbs to move in close relation to the experimental animal. Initially, the direction that the avatar is facing will be dictated by the monkey moving a handlebar with its hands. As the animal moves the handlebar left or right, the avatar will rotate in the corresponding direction. The avatar’s legs will mimic the exact motion of the monkey’s legs on the treadmill. The simplest task will be for the animal to simply move the avatar forward to an object that represents a reward, a virtual fruit. Virtual fruits will appear at different angular positions relative to the monkey, which will let us measure the neuronal representation of navigation direction and modulations in cortical arm representation related to the steering. The monkey will have to make several steps while steering in the required direction to approach a virtual reward and to obtain an actual reward. The next set of experiments will allow the animal to control the virtual BMI in a manner similar to how we anticipate that the eventual application will be used: with no active movement of the subject’s body parts. The animals will use the neural control of the environment to obtain rewards when they are seated in a monkey chair. We expect that the monkey will be able to generate periodic neural modulations associated with individual steps of the avatar even though it does not perform any actual steps with its own legs.