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.

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.

The neural mechanisms of anesthetic-induced unconsciousness have yet to be fully elucidated, in part because of the diverse molecular targets of anesthetic agents. We demonstrate, using intracortical recordings in macaque monkeys, that information transfer between structurally connected cortical regions is disrupted during ketamine anesthesia, despite preserved primary sensory representation. Furthermore, transfer entropy, an information-theoretic measure of directed connectivity, decreases significantly between neuronal units in the anesthetized state. This is the first direct demonstration of a general anesthetic disrupting corticocortical information transfer in the primate brain. Given past studies showing that more commonly used GABAergic drugs inhibit surrogate measures of cortical communication, this finding suggests the potential for a common network-level mechanism of anesthetic-induced unconsciousness.

Objective For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials (“spikes”) requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. Approach We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position. Results We found the highest performance for thresholding when placing a threshold between −3 to −4.5*VRMS. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data was thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. Significance For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.

Objective: To date, many brain-machine interface (BMI) studies have developed decoding algorithms for neuroprostheses that provide users with precise control of upper arm reaches with some limited grasping capabilities. However, comparatively few have focused on quantifying the performance of precise finger control. Here we expand upon this work by investigating online control of individual finger groups.Approach: We have developed a novel training manipulandum for non-human primate (NHP) studies to isolate the movements of two specific finger groups: index and middle-ring-pinkie (MRP) fingers. We use this device in combination with the ReFIT (Recalibrated Feedback Intention-Trained) Kalman filter to decode the position of each finger group during a single degree of freedom task in two rhesus macaques with Utah arrays in motor cortex. The ReFIT Kalman filter uses a two-stage training approach that improves online control of upper arm tasks with substantial reductions in orbiting time, thus making it a logical first choice for precise finger control.Results: Both animals were able to reliably acquire fingertip targets with both index and MRP fingers, which they did in blocks of finger group specific trials. Decoding from motor signals online, the ReFIT Kalman filter reliably outperformed the standard Kalman filter, measured by bit rate, across all tested finger groups and movements by 31.0 and 35.2%. These decoders were robust when the manipulandum was removed during online control. While index finger movements and middle-ring-pinkie finger movements could be differentiated from each other with 81.7% accuracy across both subjects, the linear Kalman filter was not sufficient for decoding both finger groups together due to significant unwanted movement in the stationary finger, potentially due to co-contraction.Significance: To our knowledge, this is the first systematic and biomimetic separation of digits for continuous online decoding in a NHP as well as the first demonstration of the ReFIT Kalman filter improving the performance of precise finger decoding. These results suggest that novel nonlinear approaches, apparently not necessary for center out reaches or gross hand motions, may be necessary to achieve independent and precise control of individual fingers.

This study suggests that scarring does not cause an electrical problem with regard to signal quality since it does not appear to be the main contributor to increasing impedance or significantly affect amplitude unless it displaces neurons. This, in turn, suggests that neural signals can be obtained reliably despite scarring as long as the recording site has sufficiently low impedance after accumulating a thin layer of biofouling. Therefore, advancements in microelectrode technology may be expedited by focusing on improvements to the recording site-tissue interface rather than elimination of the glial scar.

The RPNI signal strength, stability, and longevity demonstrated here represents a promising method for controlling advanced prosthetic limbs and fully restoring natural movement.