CLINICS 2011;66(S1):25-32 would be superior to current designs in both the specificity and long-term performance of the sensory loop and the quality of neuronal recordings. We are currently building a prototype optogenetic implant that will simultaneously sample the activity of cortical neuronal ensembles and deliver complex stimulation patterns through multichannel optogenetic stimulation of cortical sensory areas. Our optogenetic arrays consist of both recording electrodes and fibers for optical stimulation. This design uses a novel light delivery system in which light pulses from a laser are directed onto a Digital Micromirror Device. These DMD chips consist of over 150,000 individually movable micro- meter size mirrors. Depending on the alignment of these mirrors, they will either allow the light to bypass the chip or reflect it into the components of a multicore fiberoptic, therefore allowing incredibly sophisticated patterns of activation. The multicore fibers consist of 30,000 individual strands. A number of these strands will be attached to movable electrode shafts allowing them to penetrate deep into the cortical columnar structure, thus negating the problem of light scatter by the tissue and allow very specific targeting of individual neurons. After the Chr-2 gene is delivered to the somatosensory and motor cortex of our monkeys through adeno-associated virus vector (AAV) injections performed using implanted cannulae, combined optical-stimulation/recording grids will be inserted in the cortex through the same cannulae. We expect that implanted animals will learn to use the spatiotemporal optogenetic stimulation delivered to their S1 as BMI feedback. We will integrate this artificial sensory channel into our real-time BMI system, in which monkeys control the movements of primate virtual bodies, known as avatars, directly by brain activity while receiving sensory input through spatiotemporal optogenetic stimulation. BIMANUAL BMI So far, BMIs for arm movements involved only a single actuator that typically enacted reaching and grasping movements. Future BMIs will have to generate bimanual functionality. In their truest sense, bimanual movements are temporally and spatially coordinated movements of both upper limbs. At the DUCN, we are designing a BMI capable of extracting the motor commands to enact such coordina- tion. The starting point in developing a bimanual BMI is to understand the underlying cortical processes. Bimanual processes activate different subsets of neurons than unim- anual tasks.54–59 As previous BMI research has focused on unimanual tasks, a key component of transitioning to successful bimanual control will be recognizing and adapt- ing to the differences in the cortical representation of this behavior. Unimanual movement initiation and coordination have often been shown to be correlated to neural activity in the contralateral hemisphere of the motor cortex. However, in tasks involving both arms, activated cortical areas are less clearly defined and have greater interhemispheric interac- tions. Interhemispheric connections of premotor regions have been the subject of several investigations, and several findings point to the fact that these regions exhibit much greater interhemispheric connectivity than M1.59 At least five cortical regions show activation related to inter-limb coordination: dorsal premotor cortex (PMd); cingulate motor area (CMA); supplementary motor area (SMA); 29 Brain-machine interface research Mikhail AL et al. posterior parietal cortex (PPC); and M1.60 We will implant these areas to build the first bimanual BMI. In our bimanual BMI experiment, monkeys have to move two avatar hands within an initial target that appears in the center of the screen. When this task is learned, the monkey will learn a similar behavior, except that two peripheral targets will appear, and the monkeys will have to guide each avatar hand to the separate targets. We expect that monkeys will eventually learn to control such bimanual movements through a BMI, without engaging any overt movements of their limbs. BIPEDAL LOCOMOTION Previous BMI studies focused predominantly on the behavioral tasks in which an artificial actuator enacted upper extremity movements, such as reaching and grasping. Except for a few studies,24,61,62 virtually no attempts have been made to translate BMI technology to tasks enacting motor functionality of lower extremities. Yet, deficits or the complete loss of the ability to walk present a considerable problem. Such deficits commonly result from spinal cord injury,63–65 neurologic diseases66,67 and limb loss.68 Surveys of paraplegic patients showed that they prioritized walking and trunk stability among the most desired motor functions they would like to be restored.69,70 Quadriplegic patients prioritized arm and hand function.69 Thus, developing neural prosthetic devices for restoration of leg mobility is as important as developing neuroprosthetics for resuming arm and hand movements, and for some categories of patients it is their top priority. Such neuroprosthetic devices would clearly have a major impact on the community of patients suffering from leg paralysis. In our treadmill locomotion setup, rhesus macaques walked bipedally on a custom modified treadmill.24 We tracked movements of the right legs of the monkeys using a wireless, video-based tracking system developed in our laboratory.71 We then added a second tracking system to the setup and tracked movements of both legs. Leg kinematics and electro- myographies (EMGs) of leg muscles were reconstructed from neuronal ensemble activity in real time using the Wiener filter.13,17,26,72 While the individual neuronal firing rates were highly variable from step to step at the millisecond scale, combining the activity of many neurons using a BMI decoder produced accurate predictions of leg movements. Extraction of neural information performed separately for different cortical areas showed that walking parameters could be predicted using neuronal activity recorded in either M1 or S1 contral- ateral to the right leg, as well as ipsilateral M1. In these experiments, neuronal ensemble activity was simultaneously recorded from all 384 channels. Using the locomotion setup, we also demonstrated real-time BMI control of bipedal walking in a robot.27 We sent the predictions of monkey leg kinematics to the Advanced Telecommunications Research Institute (ATR), Kyoto, Japan, where our collaborators set their humanoid 51-degree of freedom robot (manufactured by SARCOS, Salt Lake City, UT, USA) to reproduce monkey locomotion patterns. The monkey received visual feedback of the robot’s movements on a video screen. POSTURE AND BALANCE BMI for restoration of lower limb function is fundamen- tally different from the BMI for upper limbs because it has