Afsheen Afshar

A high-performance brain–computer interface

Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain–computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or ∼15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

A Central Source of Movement Variability

Movements are universally, sometimes frustratingly, variable. When such variability causes error, we typically assume that something went wrong during the movement. The same assumption is made by recent and influential models of motor control. These posit that the principal limit on repeatable performance is neuromuscular noise that corrupts movement as it occurs. An alternative hypothesis is that movement variability arises before movements begin, during motor preparation. We examined this possibility directly by recording the preparatory activity of single cortical neurons during a highly practiced reach task. Small variations in preparatory neural activity were predictive of small variations in the upcoming reach. Effect magnitudes were such that at least half of the observed movement variability likely had its source during motor preparation. Thus, even for a highly practiced task, the ability to repeatedly plan the same movement limits our ability to repeatedly execute the same movement.

HermesB: A Continuous Neural Recording System for Freely Behaving Primates

Chronically implanted electrode arrays have enabled a broad range of advances in basic electrophysiology and neural prosthetics. Those successes motivate new experiments, particularly, the development of prototype implantable prosthetic processors for continuous use in freely behaving subjects, both monkeys and humans. However, traditional experimental techniques require the subject to be restrained, limiting both the types and duration of experiments. In this paper, we present a dual-channel, battery-powered neural recording system with an integrated three-axis accelerometer for use with chronically implanted electrode arrays in freely behaving primates. The recording system called HermesB, is self-contained, autonomous, programmable, and capable of recording broadband neural (sampled at 30 kS/s) and acceleration data to a removable compact flash card for up to 48 h. We have collected long-duration data sets with HermesB from an adult macaque monkey which provide insight into time scales and free behaviors inaccessible under traditional experiments. Variations in action potential shape and root-mean square (RMS) noise are observed across a range of time scales. The peak-to-peak voltage of action potentials varied by up to 30% over a 24-h period including step changes in waveform amplitude (up to 25%) coincident with high acceleration movements of the head. These initial results suggest that spike-sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance. During physically active periods (defined by head-mounted accelerometer), significantly reduced 5-25-Hz local field potential (LFP) power and increased firing rate variability were observed. Using a threshold fit to LFP power, 93% of 403 5-min recording blocks were correctly classified as active or inactive, potentially providing an efficient tool for identifying different behavioral contexts in prosthetic applications. These results demonstrate the utility of the HermesB system and motivate using this type of system to advance neural prosthetics and electrophysiological experiments.

Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses

Neural prosthetic interfaces use neural activity related to the planning and perimovement epochs of arm reaching to afford brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or perimovement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and perimovement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model (HMM), in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an HMM, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity that is not accompanied by any external behavior changes, can be detected using a threshold on the a posteriori HMM state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as that by a maximum-likelihood estimator using a window of known plan activity. In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data. Thus the HMM technique for automatically detecting transitions between epochs of neural activity enables prosthetic interfaces that can operate autonomously.

Single-neuron stability during repeated reaching in macaque premotor cortex.

Some movements that animals and humans make are highly stereotyped, repeated with little variation. The patterns of neural activity associated with repeats of a movement may be highly similar, or the same movement may arise from different patterns of neural activity, if the brain exploits redundancies in the neural projections to muscles. We examined the stability of the relationship between neural activity and behavior. We asked whether the variability in neural activity that we observed during repeated reaching was consistent with a noisy but stable relationship, or with a changing relationship, between neural activity and behavior. Monkeys performed highly similar reaches under tight behavioral control, while many neurons in the dorsal aspect of premotor cortex and the primary motor cortex were simultaneously monitored for several hours. Neural activity was predominantly stable over time in all measured properties: firing rate, directional tuning, and contribution to a decoding model that predicted kinematics from neural activity. The small changes in neural activity that we did observe could be accounted for primarily by subtle changes in behavior. We conclude that the relationship between neural activity and practiced behavior is reasonably stable, at least on timescales of minutes up to 48 h. This finding has significant implications for the design of neural prosthetic systems because it suggests that device recalibration need not be overly frequent, It also has implications for studies of neural plasticity because a stable baseline permits identification of nonstationary shifts.

Free-paced high-performance brain-computer interfaces.

Neural prostheses aim to improve the quality of life of severely disabled patients by translating neural activity into control signals for guiding prosthetic devices or computer cursors. We recently demonstrated that plan activity from premotor cortex, which specifies the endpoint of the upcoming arm movement, can be used to swiftly and accurately guide computer cursors to the desired target locations. However, these systems currently require additional, non-neural information to specify when plan activity is present. We report here the design and performance of state estimator algorithms for automatically detecting the presence of plan activity using neural activity alone. Prosthesis performance was nearly as good when state estimation was used as when perfect plan timing information was provided separately ( approximately 5 percentage points lower, when using 200 ms of plan activity). These results strongly suggest that a completely neurally-driven high-performance brain-computer interface is possible.

Signal Processing Challenges for Neural Prostheses

Cortically controlled prostheses are able to translate neural activity from the cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. While both noninvasive and invasive electrode techniques can be used to measure neural activity, the latter promises considerably higher levels of performance and therefore functionality to patients. The process of translating analog voltages recorded at the electrode tip into control signals for the prosthesis requires sophisticated signal acquisition and processing techniques. In this article we briefly review the current state-of-the-art in invasive, electrode-based neural prosthetic systems, with particular attention to the advanced signal processing algorithms that enable that performance. Improving prosthetic performance is only part of the challenge, however. A clinically viable prosthetic system will need to be more robust and autonomous and, unlike existing approaches that depend on multiple computers and specialized recording units, must be implemented in a compact, implantable prosthetic processor (IPP). In this article we summarize recent results which indicate that state-of-the-art prosthetic systems can be implemented in an IPP using current semiconductor technology, and the challenges that face signal processing engineers in improving prosthetic performance, autonomy and robustness within the restrictive constraints of the IPP.

Cortical Neural Prosthesis Performance Improves When Eye Position Is Monitored

Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve prosthesis performance is to account for the direction of gaze in the operation of the prosthesis. This proposal stems from recent discoveries that the direction of gaze influences neural activity in several areas that are commonly targeted for electrode implantation in neural prosthetics. Here, we first demonstrate that neural prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from neural activity, and thus performance gains can be realized even without a device that tracks eye position.

A High Performance Neurally-Controlled Cursor Positioning System

Prior work has shown that neural activity from the primate brain can maneuver a computer cursor to specified visual targets. This cursor movement can take over a second, longer than the time for an arm reach to the same location. We asked if this acquisition time could be reduced, thereby increasing the number of targets that could be hit per second. We implemented a system that positions a prosthetic cursor at discrete locations, based on pre-movement neural activity in rhesus monkeys. Using a delayed center-out reaching task with several different target layouts, neural activity was simultaneously recorded from an electrode array implanted in the dorsal pre-motor cortex. We designed a target prediction algorithm based on maximum-likelihood models (using Gaussian or Poisson distributions) to decode the upcoming reach target in real-time. During cursor trials, the algorithm predicted the most likely reach target using 50-275 ms of delay activity starting at least 150 ms after target onset If the target prediction was correct, a cursor was positioned and the monkey received a reward. The performance of the system was evaluated based on the accuracy of decoded targets and speed at which targets were decoded, both of which were consolidated with an information theoretic analysis. The maximum average sustained rate of target acquisition was 43 targets per second obtained with a 2 target layout and 50 ms of delay activity. The maximum information transfer rate calculated for the system was 6.5 bps obtained with an 8 target layout and 100 ms of delay activity

An Autonomous, Broadband, Multi-channel Neural Recording System for Freely Behaving Primates

Successful laboratory proof-of-concept experiments with neural prosthetic systems motivate continued algorithm and hardware development. For these efforts to move beyond traditional fixed laboratory setups, new tools are needed to enable broadband, multi-channel, long duration neural recording from freely behaving primates. In this paper we present a dual-channel, battery powered, neural recording system with integrated 3-axis accelerometer for use with chronically implanted electrode arrays. The recording system, called HermesB, is self-contained, autonomous, programmable and capable of recording broadband neural and head acceleration data to a removable compact flash card for up to 48 hours