CLINICS 2011;66(S1):25-32 the shaft will be removed, leaving the electrodes in the brain. Each single electrode shaft will carry 10–20 recording microwires, staggered to cover the targeted nervous tissue. To refer properly to this new technology and to distinguish it from previous approaches, we have coined the term ‘very- large scale multichannel brain implants’ (VLS-MBI). DECODING ALGORITHMS Large-scale neuronal activity recorded by our very large- scale multichannel brain implants will be processed by real- time BMI encoding algorithms that extract behavioral parameters, for example kinematic parameters of many DOF limb movements. At the DUCN, we employ an integrated BMI suite that combines the neurophysiologic recording and stimulation hardware, as well as the computer cluster that runs behavioral tasks and BMI decoders.24,29 We have incorporated into this BMI suite several neuronal decoders: the unscented Kalman filter,36 the Wiener filter, artificial neural networks, and discrete state Bayesian approaches.38 In particular, the unscented Kalman filter makes Bayesian inferences of the repetitive patterns of the movements performed during arm reaching tasks, as such patterns will occur frequently in the tasks that a practical neuroprosthetic limb has to perform. For example, a prosthetic limb that aids in feeding moves back and forth between the food item and the user’s mouth. The unscented Kalman filter exploits non-linear models of neural tuning and prior knowledge about movement patterns. In addition, it keeps a short history of the state variables—in this case, the desired limb movements. This algorithm also captures complex patterns in the desired movements. At the DUCN, the unscented Kalman filter was tested in a BMI that enacted arm reaching movements and achieved significantly better accuracy as compared to the Kalman filter, the Wiener filter, the population vector method, and the stochastic state point process filter.38 In addition to the unscented Kalman filter, we employ a multiple-model-switching paradigm.24 In this approach, separate submodels are trained to decode particular behavioral states (e.g. the grasping phase of the reach and grasp movement versus the reaching phase, or walking forward versus walking backward), and the state predictor model serves to detect the state and select the appropriate submodel. The simplest switching model works as a combination of three linear decoding models: a model for predicting state 1 (e.g. reaching), a model for state 2 (e.g. grasping) and the paradigm predictor model (the switch). These models are arranged in two layers with the paradigm predictor model controlling a toggle between the two kinematic submodels, which are then shunted to the final output of the switching model. When it is determined that the monkey is performing in state 1, one submodel is used to produce the output, and when state 2 is detected, the other submodel is used. These algorithmic tools allow us to test different neural control modes. In the most straightforward implementation, we predict the kinematics of each joint of the limb and convert them into the actuator configuration. This control model is of interest for several reasons. First, this mode of operation is similar to the operation of the nervous system in controlling natural movements. Second, the number of degrees of freedom that can be achieved by a BMI is of great interest to those working in this field. At the same time, 27 Brain-machine interface research Mikhail AL et al. practical BMI applications will be inconvenient to use if the user is required to control each degree of freedom indepen- dently. To overcome this problem, we intend to implement in our clinical applications a mode of BMI operation in which the control over an external actuator is shared between the subject’s brain activity and robotic controls. This mode of operation is known as shared control of a BMI.39 During shared control, the subject’s brain is primarily in charge of high-order control of movements (when to initiate movement and where to move), whereas the low-level coordination of the movement is performed by an autonomous controller. Such sharing optimizes BMI performance: the user can control it through their voluntary intentions, whereas the robotic controller assures accuracy and stability. BRAIN-MACHINE-BRAIN INTERFACE The DUCN research team was the first to add an artificial somatosensory feedback loop to a BMI for arm reaching. In our initial studies, we used spatiotemporal patterns of ICMS to instruct primate arm reaches. In a study conducted in owl monkeys,40 we addressed two issues that are critical for using ICMS as an artificial sensory channel in neural prostheses: (i) whether such artificial sensation can be evoked by multichannel ICMS; and (ii) whether ICMS is suitable for long-term usage in a BMI-like application. We explored the first issue by testing the capacity of owl monkeys to discriminate multichannel ICMS of increasing complexity. We investigated the second issue by testing the long-term efficacy of ICMS over many months. We discovered that owl monkeys could learn to discriminate spatiotemporal patterns of ICMS delivered directly to their S1 and guide their reaching movements based on this discrimination. Owl monkeys were implanted with multi- electrode arrays in several cortical areas, and S1 implants were employed to deliver spatiotemporal patterns of ICMS. The behavioral task performed by the owl monkeys progressed from a simple requirement of detecting the presence of ICMS, to the requirement of discriminating spatiotemporal patterns created using four electrode pairs. The monkeys successfully learned these tasks. Interestingly, they learned new microstimulation patterns more rapidly compared with initial training. This result suggests that the monkey brain may have mimicked the operation of this intra-cortical microstimulation paradigm as if it were a new sensory channel. In a study conducted in rhesus monkeys, we employed ICMS to cue monkeys that performed BMI reaching tasks.29 The behavioral tasks consisted of acquiring visual targets with a computer cursor. The monkeys first performed the behavioral tasks manually, using a hand-held joystick, and later controlled the cursor movements directly with the electrical activity of a sample of cortical neurons. Manual performance data were used to train linear decoding models that extracted cursor position from the modulations of populations of simultaneously recorded cortical cells. Once the model parameters were calculated, the mode of operation was switched to brain control during which the joystick was disconnected from the cursor and cursor position became directly controlled by the signals extracted from the animal’s brain. ICMS of S1 was then used to cue monkeys to which direction they had to move their arms during execution of a target choice task. In this experiment, monkeys had to choose among two visually identical targets