Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals requires further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from 4 intracortical arrays in 3 animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in 3 animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for 3 of 4 arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold crossing events rather than well isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

Objective Individual carbon fiber microelectrodes can record unit activity in both acute and semi-chronic (∼1 month) implants. Additionally, new methods have been developed to insert a 16 channel array of carbon fiber microelectrodes. Before assessing the in vivo long-term viability of these arrays, accelerated soak tests were carried out to determine the most stable site coating material. Next, a multi-animal, multi-month, chronic implantation study was carried out with carbon fiber microelectrode arrays and silicon electrodes. Approach Carbon fibers were first functionalized with one of two different formulations of PEDOT and subjected to accelerated aging in a heated water bath. After determining the best PEDOT formula to use, carbon fiber arrays were chronically implanted in rat motor cortex. Some rodents were also implanted with a single silicon electrode, while others received both. At the end of the study a subset of animals were perfused and the brain tissue sliced. Tissue sections were stained for astrocytes, microglia, and neurons. The local reactive responses were assessed using qualitative and quantitative methods. Main results Electrophysiology recordings showed the carbon fibers detecting unit activity for at least 3 months with average amplitudes of ∼200 μV. Histology analysis showed the carbon fiber arrays with a minimal to non-existent glial scarring response with no adverse effects on neuronal density. Silicon electrodes showed large glial scarring that impacted neuronal counts. Significance This study has validated the use of carbon fiber microelectrode arrays as a chronic neural recording technology. These electrodes have demonstrated the ability to detect single units with high amplitude over 3 months, and show the potential to record for even longer periods. In addition, the minimal reactive response should hold stable indefinitely, as any response by the immune system may reach a steady state after 12 weeks.

Objective Single carbon fiber electrodes (d=8.4 μm) insulated with parylene-c and functionalized with PEDOT:pTS have been shown to record single unit activity but manual implantation of these devices with forceps can be difficult. Without an improvement in the insertion method any increase in the channel count by fabricating carbon fiber arrays would be impractical. In this study, we utilize a water soluble coating and structural backbones that allow us to create, implant, and record from fully functionalized arrays of carbon fibers with ~150 μm pitch. Approach Two approaches were tested for the insertion of carbon fiber arrays. The first method used a PEG coating that temporarily stiffened the fibers while leaving a small portion at the tip exposed. The small exposed portion (500 μm – 1 mm) readily penetrated the brain allowing for an insertion that did not require the handling of each fiber by forceps. The second method involved the fabrication of silicon support structures with individual shanks spaced 150 μm apart. Each shank consisted of a small groove that held an individual carbon fiber. Main results Our results showed that the PEG coating allowed for the chronic implantation of carbon fiber arrays in 5 rats with unit activity detected at 31 days post-implant. The silicon support structures recorded single unit activity in 3 acute rat surgeries. In one of those surgeries a stacked device with 3 layers of silicon support structures and carbon fibers was built and shown to readily insert into the brain with unit activity on select sites. Significance From these studies we have found that carbon fibers spaced at ~150 μm readily insert into the brain. This greatly increases the recording density of chronic neural probes and paves the way for even higher density devices that have a minimal scarring response.

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.

Peripheral nerves provide a promising source of motor control signals for neuroprosthetic devices. Unfortunately, the clinical utility of current peripheral nerve interfaces is limited by signal amplitude and stability. Here, we showed that the regenerative peripheral nerve interface (RPNI) serves as a biologically stable bioamplifier of efferent motor action potentials with long-term stability in upper limb amputees. Ultrasound assessments of RPNIs revealed prominent contractions during phantom finger flexion, confirming functional reinnervation of the RPNIs in two patients. The RPNIs in two additional patients produced electromyography signals with large signal-to-noise ratios. Using these RPNI signals, subjects successfully controlled a hand prosthesis in real-time up to 300 days without control algorithm recalibration. RPNIs show potential in enhancing prosthesis control for people with upper limb loss.

Objective Interictal high frequency oscillations (HFOs) in intracranial EEG are a potential biomarker of epilepsy, but current automated HFO detectors require human review to remove artifacts. Our objective is to automatically redact false HFO detections, facilitating clinical use of interictal HFOs. Methods Intracranial EEG data from 23 patients were processed with automated detectors of HFOs and artifacts. HFOs not concurrent with artifacts were labeled quality HFOs (qHFOs). Methods were validated by human review on a subset of 2,000 events. The correlation of qHFO rates with the seizure onset zone (SOZ) was assessed via 1) a retrospective asymmetry measure and 2) a novel quasi-prospective algorithm to identify SOZ. Results Human review estimated that less than 12% of qHFOs are artifacts, whereas 78.5% of redacted HFOs are artifacts. The qHFO rate was more correlated with SOZ (p=0.020, Wilcoxon signed rank test) and resected volume (p=0.0037) than baseline detections. Using qHFOs, our algorithm was able to determine SOZ in 60% of the ILAE Class I patients, with all algorithmically-determined SOZs fully within the resected volumes. Conclusions The algorithm reduced false-positive HFO detections, improving the precision of the HFO-biomarker. Significance These methods provide a feasible strategy for HFO detection in real-time, continuous EEG with minimal human monitoring of data quality.

Objective The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported ‘recalibrated feedback intention-trained Kalman Filter’ (ReFIT-KF). Approach This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. Main Results Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. Significance These findings highlight the utility of intention estimation in reducing the need of adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.

Objective Brain machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system. Approach We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed 5 distinct isometric hand postures, as well as 4 distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with 2 participants. Main Results Classification rates were 68%, 84%, and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and characterization of non-stationarities such that ECoG could be a viable signal source for grasp control for amputees or individuals with paralysis.