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Open-source Toolkit: Benchtop Carbon Fiber Microelectrode Array for Nerve Recording

Conventional peripheral nerve probes are primarily fabricated in a cleanroom, requiring the use of multiple expensive and highly specialized tools. This ...

University of Michigan, Ann Arbor

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.

Universal automated high frequency oscillation detector for real-time, long term EEG.

High Frequency Oscillations (HFOs) are being studied as a biomarker of epilepsy, yet it is unknown how various acquisition parameters at different centers affect detection and analysis of HFOs. This paper specifically quantifies effects of sampling rate (FS) and anti-aliasing filter (AAF) positions on automated HFO detection.

Effect of sampling rate and filter settings on High Frequency Oscillation detections.

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.

A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees.

The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.

Nitrous oxide (NO) is a hypnotic gas with antidepressant and psychedelic properties at subanesthetic concentrations. Despite long-standing clinical use, there is insufficient understanding of its effect on neural dynamics and cortical processing, which is important for mechanistic understanding of its therapeutic effects. We administered subanesthetic (70%), inhaled NO and studied the dynamic changes of spiking rate, spectral content, and somatosensory information representation in primary motor cortex (M1) in two male rhesus macaques implanted with Utah microelectrode arrays in the hand area of M1. The average sorted multiunit spiking rate in M1 increased from 8.1 ± 0.99 to 10.6 ± 1.3 Hz in Monkey W (<0.001) and from 5.6 ± 0.87 to 7.0 ± 1.1 Hz in Monkey N (=0.003). Power spectral densities increased in beta- and gamma-band power. To evaluate somatosensory content in M1 as a surrogate of information transfer, fingers were lightly brushed and classified using a naive Bayes classifier. In both monkeys, the proportion of correctly classified fingers dropped from 0.50 ± 0.06 before NO inhalation to 0.34 ± 0.03 during NO inhalation (=0.018), although some fingers continued to be correctly classified (=0.005). The decrease in correct classifications corresponded to decreased modulation depth for the population (=0.005) and fewer modulated units (=0.046). However, the increased single-unit firing rate was not correlated with its modulation depth (  < 0.001, =0.93). These data suggest that NO degrades information transfer, although no clear relationship was found between neuronal tuning and NO-induced changes in firing rate.

Neural Dynamics in Primate Cortex during Exposure to Subanesthetic Concentrations of Nitrous Oxide.

Brain-machine interfaces (BMI) are being developed to restore upper limb function for persons with spinal cord injury or other motor degenerative conditions. BMI and implantable sensors for myoelectric prostheses directly extract information from the central or peripheral nervous system to provide users with high fidelity control of their prosthetic device. Control algorithms have been highly transferable between the 2 technologies but also face common issues. In this review of the current state of the art in each field, the authors point out similarities and differences between the 2 technologies that may guide the implementation of common solutions to these challenges.

Brain-Machine Interfaces: Lessons for Prosthetic Hand Control.

Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

Cynthia A. Chestek

Department of Biomedical Engineering,
Department of Electrical Engineering and Computer Science,
Neuroscience Graduate Program,
Robotics Graduate Program

University of Michigan, Ann Arbor

Open-source Toolkit: Benchtop Carbon Fiber Microelectrode Array for Nerve Recording

Cynthia A. Chestek

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Biomedical Engineering,Department of Electrical Engineering and Computer Science,Neuroscience Graduate Program,Robotics Graduate Program

University of Michigan, Ann Arbor

Open-source Toolkit: Benchtop Carbon Fiber Microelectrode Array for Nerve Recording

Department of Biomedical Engineering,
Department of Electrical Engineering and Computer Science,
Neuroscience Graduate Program,
Robotics Graduate Program