Brain-machine interface research Mikhail AL et al. its perspectives and strategic plan for the near- and long-term future of BMI research. BMI COMPONENTS The essential components of a BMI system are well captured by the BMI that enacts reaching and grasp- ing.7,9,13,17,26 In this BMI design, a rhesus monkey controls a robotic arm with its motor cortical activity, while visual and/or somatosensory feedback signals from the robot are delivered back to the brain as either natural visual stimuli or, in the case of artificial tactile information, intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1).7,28,29 In these studies, we implant multielectrode arrays in multiple cortical areas of the rhesus monkey’s brain.30 The present generation of chronic multielectrode implants is capable of recording the extracellular electrical activity of hundreds of cortical cells.13,24,26,30 As a result of recent technological developments, this benchmark number is expected to rise to several thousands of simultaneously recorded neurons in the next decade. Recording such large- scale neuronal ensemble activity is crucially important for BMI accuracy.9 Concurrent neuronal ensemble activity is processed by BMI decoding algorithms which translate myriad neuronal spikes into continuous signals that drive the robotic arm’s movements, according to the voluntary motor intentions of the subject. The BMI setup also includes the data acquisition system, the computer cluster running multiple decoding models in real time, the robot arm, the visual display and a sensory feedback loop from the actuator to the brain. Below we discuss these key BMI components in more detail. LARGE SCALE NEURONAL RECORDINGS Our work on BMIs has clearly demonstrated that a large number of recording channels is needed for accurate extraction of motor intentions from the brain.9,13,17,31,32 Shifting from the classic focus on single neurons, today, more and more evidence accumulates in favor of the notion that distributed ensembles of neurons define the true physiologic unit of the mammalian central nervous system.9 During the last two decades, advanced electrophysiological methods have allowed recording from progressively larger samples of single neurons in behaving animals.30,33–35 This methodology has equipped neuroscientists with better tools to study the neurophysiologic principles that define the operation of the cortex,9 and has also made the idea of BMIs practical. Today, the most advanced BMIs developed at the DUCN utilize simultaneous recordings of the extracellular electrical activity of hundreds of individual neurons.30 Neuron-dropping analysis, a technique developed at the DUCN to measure the dependence of parameter extraction accuracy on the number of simultaneously recorded neurons,17 shows that BMI accuracy increases with the size of the neuronal ensemble. Typically, the best extractions of motor parameters are obtained when the activity of populations of neurons from several cortical areas is recorded simultaneously.13,24 Importantly, the number of required neurons increases as more motor parameters are simultaneously extracted.24 Large neural ensembles confer redundancy of control and, hence, reliability.9 In our current research on BMIs that enact arm and leg movements, we can obtain a peak signal-to-noise ratio (SNR) of 10dB using approximately 100 26 CLINICS 2011;66(S1):25-32 task related neurons.24,36 This corresponds to a 5 bits/s bandwidth. SNR can be further improved via time averaging, but this slows down the movements. Realistic and useful movements should have a SNR of about 20 to 30dB. Indeed, we consider this benchmark as the lower bound that one should aim in order to build a neuropros- thetic device for clinical use. Thus, to estimate the number of neurons needed to obtain this level of accuracy, we assume that noise decreases as the inverse of the square root of the number of neurons—an assumption that holds for both Gaussian and Poisson noise sources. Under this assumption, a tenfold increase in the sample of neurons recorded simultaneously would be needed to achieve the 10dB of improvement needed. Therefore, to obtain a control signal with 20dB fidelity (the DUCN short-term goal), recordings involving 1000 neurons are needed; to obtain 40dB (the DUCN long-term goal), 100,000 neurons are needed. In addition, it should be emphasized that commensurately more neurons are required for prosthetic limbs with many degrees of freedom (DOFs). As practical neuroprosthetics for paralyzed subjects will have to cope with at least 4–10 DOF, demands for large samples of recorded neurons will remain a central bottleneck for the development of clinical applications of BMIs for the foreseeable future. Thus, only when a new generation of high-channel count recordings becomes available can practical neuroprosthetics for clinical use be implemented with a reasonable expectation for clinical success. At the DUCN, we expect to conclude the development of such new technology and, as a conse- quence, launch the testing phase of our first clinical application of a BMI for restoring full body mobility by the summer of 2014. To achieve this goal, we have been developing advanced sensors that sample large-scale extracellular electrical activity from the brain. In our most recent design of multielectrode implants, electrodes are arranged into sub- sets sitting inside guiding tubes.37 The electrodes within these subsets have different lengths. The guiding tubes are spaced at 0.5–2 mm, and each of them is independently movable. This new three-dimensional (3D) design, named ‘the multi-electrode recording cube’ not only improves the size of the potential neuronal sample recorded simulta- neously per probe, but also enhances the implantation capacity of the microelectrodes in the brain tissue as each electrode subset penetrates individually, minimizing corti- cal dimpling. Currently, we are expanding this new cube design to develop the next generation of implants that will increase the number of potential active recording sites per cube to about 1500. Also, as the size of each implanted microelectrode is of vital importance when so many electrodes are inserted into richly vascularized brain tissue, it is imperative to produce as little tissue displacement as possible during the surgical implantation of these recording devices. This issue will be resolved by removing the structural elements after implantation, thereby freeing the microelectrodes made of smaller diameter microwires than that required to pierce and penetrate the brain tissue. Not only will small diameter microwires minimize the mechan- ical distortion of nervous tissue, it will also help to minimize microglial and other immune responses to the foreign material. Our approach will allow implantation of small- diameter wires and thereby avoid failures associated with large-diameter implants. Fine electrodes will be guided into the brain with a strong, stiff tungsten central shaft. Later on,