Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.

Objective.While brain-machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e. consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.Mainresults.First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below5%, it was possible to achieve mean true positive rate of28.1%online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.Significance.Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements may be obtained by enhancing classification and correction strategies.

Shifts in Estimated Preferred Directions During Simulated BMI Experiments With No Adaptation.

Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis. Simulations are based on the assumption that the brain implements optimal state estimation and feedback control and that cortical motor neurons encode the estimated state and control vector. Our simulations successfully reproduce apparent shifts in EPDs observed in BMI experiments with different BMI filters, including linear, Kalman and re-calibrated Kalman filters, even with no neural adaptation. Theoretical analysis identifies the conditions for reducing those shifts. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. We conclude that the observed shifts in EPDs may result from experimental conditions, and in particular correlated velocities or tuning weights, even with no adaptation. Under the above assumptions, we show that if neurons are tuned differently to the estimated velocity, estimated position and control signal, the EPD with respect to actual velocity may not capture the real PD in which the neuron encodes the estimated velocity. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments.

Error related EEG potentials evoked by visuo-motor rotations.

Electroencephalographic (EEG) correlates of errors, known as error-related potentials (ErrPs), provide promising tools to investigate error processing in the brain and to detect and correct errors induced by brain-computer interfaces (BCIs). Visuo-motor rotation (VMR) is a well-known experimental paradigm to introduce visuo-motor errors that closely mimics directional errors induced by BCIs. However, investigations of ErrPs during VMR experiments are limited and reveals different ErrPs depending on task and synchronization. We conducted VMR experiments with 5 randomly selected conditions (no-rotation, small, ±22.5°, or large, ±45° rotations) to hamper adaptation and facilitate investigation of the effect of error size. We tracked eye movements so EEG was synchronized not only to onset of movement correction (OMC) but also to saccadic movement onset (SMO). Kinematic analysis indicated that maximum deviations from a straight line to the target were larger in trials with large rotations compared to small or no rotations, but there was a large overlap. Thus, we also compared ErrPs generated by trials with different maximum deviations. Our results reveal that trials with large rotations and especially trials with large maximum deviations evoke a significant positive ErrP component. The positive peak appeared 380 msec after SMO and 240 msec after OMC. Furthermore, the positive peak was associated with activity in Brodmann areas 5 and 7, in agreement with other studies and with the role of posterior parietal cortex in reaching movements. The observed ErrP may facilitate further investigation of error processing in the brain and error detection and correction in BCIs.

Effects of model inaccuracies on reaching movements with intermittent control.

BACKGROUND AND OBJECTIVES: Human motor control (HMC) has been hypothesized to involve state estimation, prediction and feedback control to overcome noise, delays and disturbances. However, the nature of communication between these processes, and, in particular, whether it is continuous or intermittent, is still an open issue. Depending on the nature of communication, the resulting control is referred to as continuous control (CC) or intermittent control (IC). While standard HMC theories are based on CC, IC has been argued to be more viable since it reduces computational and communication burden and agrees better with some experimental results. However, to be a feasible model for HMC, IC has to cope well with inaccurately modeled plants, which are common in daily life, as when lifting lighter than expected loads. While IC may involve event-driven triggering, it is generally assumed that refractory mechanisms in HMC set a lower limit on the interval between triggers. Hence, we focus on periodic IC, which addresses this lower limit and also facilitates analysis. THEORETICAL METHODS AND RESULTS: Theoretical stability criteria are derived for CC and IC of inaccurately modeled linear time-invariant systems with and without delays. Considering a simple muscle-actuated hand model with inaccurately modeled load, both CC and IC remain stable over most of the investigated range, and may become unstable only when the actual load is much smaller than expected, usually smaller than the minimum set by the actual mass of the forearm and hand. Neither CC nor IC is consistently superior to the other in terms of the range of loads over which the system remains stable. NUMERICAL METHODS AND RESULTS: Numerical simulations of time-delayed reaching movements are presented and analyzed to evaluate the effects of model inaccuracies when the control and observer gains are time-dependent, as is assumed to occur in HMC. Both IC and CC agree qualitatively with previously published experimental results with inaccurately modeled plants. Thus, our study suggests that IC copes well with inaccurately modeled plants and is indeed a viable model for HMC.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.