Current Brain-machine Interfaces Research Articles

Flexible and smart electronics for single-cell resolved brain–machine interfaces

Brain–machine interfaces (BMIs) offer the potential for the development of communication tools between the brain and external devices. The current BMI technologies for recording and modulation of electric signals from the brain have made significant contributions to areas such as neuroscience, disease diagnosis, and rehabilitation. Next-generation BMIs require long-term stable recording and modulation of electrical signals from statistically significant neuron populations with millisecond single-cell spatiotemporal resolution. However, there are challenges to achieving this stability due to the mechanical and geometrical mismatches between electronics and the brain tissue. In addition, the requirement to achieve cell-type-specific neuromodulation and transmit and process the ever-increasing volume of data on-the-fly necessitates the implementation of smart electronics. In this review, we first summarize the requirements, challenges, and current limitations of BMIs. We then highlight three major approaches to the fabrication of flexible electronics as implantable electronics, aimed at enabling long-term stable and gliosis-free BMIs. The progress of multifunctional electronics for multimodal recording and modulation of cell-type-specific components in the brain is also discussed. Furthermore, we discuss the integration of wireless and closed-loop modulation, and on-chip processing as smart electronic components for BMIs. Finally, we examine the remaining challenges in this field and the future perspectives for how flexible and smart electronics can address these problems and continue to advance the field of BMIs.

Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface.

Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Experimental results demonstrate the superiority of DyEnsemble in online BMI control. DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.

Robust neural decoding by kernel regression with Siamese representation learning

Objective. Brain–machine interfaces (BMIs) provide a direct pathway between the brain and external devices such as computer cursors and prosthetics, which have great potential in motor function restoration. One critical limitation of current BMI systems is the unstable performance, partly due to the variability of neural signals. Studies showed that neural activities exhibit trial-to-trial variability, and the preferred direction of neurons frequently changes under different conditions. Therefore, a fixed decoding function does not work well. Approach. To deal with the problems, we propose a novel kernel regression framework. The nonparametric kernel regression is used to fit diverse decoding functions by finding similar neural patterns to handle neural variations caused by varying tuning functions. Further, the representations of raw neural signals are learned by Siamese networks and constrained by kinematic parameters, which can alleviate neural variations caused by intrinsic noises and task-irrelevant information. The representations are jointly learned with the kernel regression framework in an end-to-end manner so that neural variations can be tackled effectively. Main results. Experiments on two datasets demonstrate that our approach outperforms most existing methods and significantly improves the robustness in challenging situations such as limited samples and missing channels. Significance. The proposed approach demonstrates robust performance with different conditions and provides a new and inspiring perspective toward robust BMI control.

Generative Decoding of Intracortical Neuronal Signals for Online Control of Robotic Arm to Intercept Moving Objects

Brain-machine interfaces (BMIs) are being developed for translating neural signals to control an external device such as a robotic arm. The prevalent decoding strategy of current BMIs is the continuously conversion of neural spike trains to motor variables via a discriminative algorithm, but this typically results in un-natural and slow robotic motion. Recently, we implemented a BMI under feedforward control for interception of moving targets, though the decoding accuracy was insufficient for accurate online control. To improve the BMI performance, a generative algorithm called Latent Factor Analysis via Dynamical Systems (LFADS) [1], was applied to data pre-processing for de-noising. The results indicate potential advantages of feedforward control with generative decoding in improving BMI design.

Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research

ABSTRACTResults that do not confirm expectations are generally referred to as ‘negative’ results. While essential for scientific progress, they are too rarely reported in the literature – Brain–Machine Interface (BMI) research is no exception. This led us to organize a workshop on BMI negative results during the 2018 International BCI meeting. The outcomes of this workshop are reported herein. First, we demonstrate why (valid) negative results are useful, and even necessary for BMIs. These results can be used to confirm or disprove current BMI knowledge, or to refine current theories. Second, we provide concrete examples of such useful negative results, including the limits in BMI-control for complete locked-in users and predictors of motor imagery BMI performances. Finally, we suggest levers to promote the diffusion of (valid) BMI negative results, e.g. promoting hypothesis-driven research using valid statistical tools, organizing special issues dedicated to BMI negative results, or convincing institutions and editors that negative results are valuable.

A fast intracortical brain–machine interface with patterned optogenetic feedback

Objective. The development of brain–machine interfaces (BMIs) brings new prospects to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback. Approach. Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (vM1) and delivers feedback simultaneously via a low-latency, high-refresh-rate, spatially structured photo-stimulation of the whisker primary somatosensory cortex (vS1), based on a mapping obtained by intrinsic imaging. Main results. We demonstrate the operation of the loop and show that mice can detect the neuronal spiking in vS1 triggered by the photo-stimulations. Finally, we show that the mice can learn a behavioral task relying solely on the artificial inputs and outputs of the closed-loop BMI. Significance. This is the first motor BMI that includes a short-latency, intracortical, somatosensory-like feedback. It will be a useful platform to discover efficient cortical feedback schemes towards future human BMI applications.

Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner.

Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees.

Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration. In this study we tested if transradial amputees (A1 and A2) could control grasp preshaping in a prosthetic device using a noninvasive electroencephalography (EEG)-based closed-loop BMI system. Participants attempted to grasp presented objects by controlling two grasping synergies, in 12 sessions performed over 5 weeks. Prior to closed-loop control, the first six sessions included a decoder calibration phase using action observation by the participants; thereafter, the decoder was fixed to examine neuroprosthetic performance in the absence of decoder recalibration. Ability of participants to control the prosthetic was measured by the success rate of grasping; ie, the percentage of trials within a session in which presented objects were successfully grasped. Participant A1 maintained a steady success rate (63±3%) across sessions (significantly above chance [41±5%] for 11 sessions). Participant A2, who was under the influence of pharmacological treatment for depression, hormone imbalance, pain management (for phantom pain as well as shoulder joint inflammation), and drug dependence, achieved a success rate of 32±2% across sessions (significantly above chance [27±5%] in only two sessions). EEG signal quality was stable across sessions, but the decoders created during the first six sessions showed variation, indicating EEG features relevant to decoding at a smaller timescale (100ms) may not be stable. Overall, our results show that (a) an EEG-based BMI for grasping is a feasible strategy for further investigation of prosthetic control by amputees, and (b) factors that may affect brain activity such as medication need further examination to improve accuracy and stability of BMI performance.

Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex.

Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.

Advancing brain-machine interfaces: moving beyond linear state space models.

Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.

Deafferented controllers: a fundamental failure mechanism in cortical neuroprosthetic systems.

Brain-machine interface (BMI) research assumes that patients with disconnected neural pathways could naturally control a prosthetic device by volitionally modulating sensorimotor cortical activity usually responsible for movement coordination. However, computational approaches to motor control challenge this view. This article examines the predictions of optimal feedback control (OFC) theory on the effects that loss of motor output and sensory feedback have on the normal generation of motor commands. Example simulations of unimpaired, totally disconnected, and deafferented controllers illustrate that by neglecting the dynamic interplay between motor commands, state estimation, feedback and behavior, current BMI systems face translational challenges rooted in a debatable assumption and experimental models of limited validity.

Invasive brain–machine interfaces: a survey of paralyzed patients’ attitudes, knowledge and methods of information retrieval

Objective. Brain–machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Approach. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. Main results. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Significance. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

Objective. Brain–machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

Selective visual attention to drive cognitive brain-machine interfaces: from concepts to neurofeedback and rehabilitation applications.

Brain–machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal.

A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.

Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

Detection of Error Related Neuronal Responses Recorded by Electrocorticography in Humans during Continuous Movements

BackgroundBrain-machine interfaces (BMIs) can translate the neuronal activity underlying a user’s movement intention into movements of an artificial effector. In spite of continuous improvements, errors in movement decoding are still a major problem of current BMI systems. If the difference between the decoded and intended movements becomes noticeable, it may lead to an execution error. Outcome errors, where subjects fail to reach a certain movement goal, are also present during online BMI operation. Detecting such errors can be beneficial for BMI operation: (i) errors can be corrected online after being detected and (ii) adaptive BMI decoding algorithm can be updated to make fewer errors in the future.Methodology/Principal FindingsHere, we show that error events can be detected from human electrocorticography (ECoG) during a continuous task with high precision, given a temporal tolerance of 300–400 milliseconds. We quantified the error detection accuracy and showed that, using only a small subset of 2×2 ECoG electrodes, 82% of detection information for outcome error and 74% of detection information for execution error available from all ECoG electrodes could be retained.Conclusions/SignificanceThe error detection method presented here could be used to correct errors made during BMI operation or to adapt a BMI algorithm to make fewer errors in the future. Furthermore, our results indicate that smaller ECoG implant could be used for error detection. Reducing the size of an ECoG electrode implant used for BMI decoding and error detection could significantly reduce the medical risk of implantation.

FPGA implementation of Kalman filter for neural ensemble decoding of rat's motor cortex

High performance computation is critical for brain–machine interface (BMI) applications. Current BMI decoding algorithms are always implemented on personal computers (PC) which affect the performance of complex mapping models. In this paper, an FPGA implementation of Kalman filter (KF) algorithm is proposed as a new computational method. The neural ensemble activities are recorded from motor cortex of rats performing a lever-pressing task for water reward. Kalman filter, which is used for mapping neural activities to kinematic variables, is implemented both on PC (MATLAB-based) and FPGA. In FPGA architecture, the row/column-based method is adopted for the matrix operation instead of the traditional element-based method, parallel and pipelined structures are also used for efficient computation at the same time. The results show that the FPGA-based implementation runs 24.45 times faster than the PC-based counterpart while achieving the same accuracy. Such a hardware-based computational method provides a tool for high-performance computation, with profound implications for portable BMI application.

Toward a high-performance, robust brain-machine interface

Event Abstract Back to Event Toward a high-performance, robust brain-machine interface Krishna V. Shenoy1* 1 Stanford University, Depts. of Electrical Engineering, Bioengineering, and Neurobiology, United States Brain-machine interfaces (BMIs) translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, offering disabled patients greater interaction with the world. BMIs have recently demonstrated considerable promise in proof-of-concept animal experiments and in human clinical trials. However, at least two critical barriers to successful translation remain. First, current BMIs are considerably slower and have less accurate control than the native arm. Second, current BMIs do not sustain performance across hours and days, or across behavioral tasks without human intervention. To address this need for increased performance and robustness, we recently conducted BMI experiments with rhesus monkeys implanted with electrode arrays in motor cortices. Initial experiments informed the design and training of a control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF) that mathematically incorporates the feedback-control nature of BMIs. Subsequent experiments demonstrated that the ReFIT-KF algorithm approximately doubles performance relative to state-of-the-art existing BMIs, and approaches native arm performance for cursor control tasks. This BMI also demonstrated robustness across years and behavioral-contexts. Taken together, this new level of performance and robustness should help increase the clinical viability of BMIs. Keywords: Motor Decoding and Brain-Machine Interfaces Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Keynote Topic: other Citation: Shenoy KV (2011). Toward a high-performance, robust brain-machine interface. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00005 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 26 Sep 2011; Published Online: 04 Oct 2011. * Correspondence: Prof. Krishna V Shenoy, Stanford University, Depts. of Electrical Engineering, Bioengineering, and Neurobiology, Stanford, United States, shenoy@stanford.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Krishna V Shenoy Google Krishna V Shenoy Google Scholar Krishna V Shenoy PubMed Krishna V Shenoy Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.