Grant A. McCallum

3-D microfabricated electrodes for targeted deep brain stimulation

This work presents a novel 4-sided, 16-channel deep brain stimulation electrode with a custom flexible high-density lead for connectivity with pulse generation electronics. The 3-dimensional electrode enables steering the current field circumferentially. The electrode is fabricated in pieces by micromachining and microfabrication techniques; the pieces are then assembled mechanically to form the electrode, after which the lead is connected. The electrode is modeled by finite element analysis and tested in vitro to validate the design concept, i.e., targeted stimulation. Simulation and experimental results for a targeted stimulation show close agreement. With a symmetric bipolar stimulation configuration, within a 3 mm radius, the electric potential in front of the activated side is at least 3.6 times larger than that on the corresponding two adjacent, not-activated sides, and 9 times larger than the corresponding opposite, not-activated side.

Chronic interfacing with the autonomic nervous system using carbon nanotube (CNT) yarn electrodes

The ability to reliably and safely communicate chronically with small diameter (100–300 µm) autonomic nerves could have a significant impact in fundamental biomedical research and clinical applications. However, this ability has remained elusive with existing neural interface technologies. Here we show a new chronic nerve interface using highly flexible materials with axon-like dimensions. The interface was implemented with carbon nanotube (CNT) yarn electrodes to chronically record neural activity from two separate autonomic nerves: the glossopharyngeal and vagus nerves. The recorded neural signals maintain a high signal-to-noise ratio (>10 dB) in chronic implant models. We further demonstrate the ability to process the neural activity to detect hypoxic and gastric extension events from the glossopharyngeal and vagus nerves, respectively. These results establish a novel, chronic platform neural interfacing technique with the autonomic nervous system and demonstrate the possibility of regulating internal organ function, leading to new bioelectronic therapies and patient health monitoring.

Ultra-low noise miniaturized neural amplifier with hardware averaging

OBJECTIVE Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (<3 μVrms 700 Hz-7 kHz), thereby requiring a baseline noise of less than 1 μVrms for a useful signal-to-noise ratio (SNR). Flat interface nerve electrode (FINE) contacts alone generate thermal noise of at least 0.5 μVrms therefore the amplifier should add as little noise as possible. Since mainstream neural amplifiers have a baseline noise of 2 μVrms or higher, novel designs are required. APPROACH Here we apply the concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments. MAIN RESULTS We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating <1.5 μVrms total recording baseline noise when connected to a FINE placed on the sciatic nerve of an awake animal. An algorithm was introduced to find the value of N that can minimize both the power consumption and the noise in order to design a miniaturized ultralow-noise neural amplifier. SIGNIFICANCE These results demonstrate the efficacy of hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.

Model-based Bayesian signal extraction algorithm for peripheral nerves

OBJECTIVE Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. APPROACH To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. MAIN RESULTS The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. SIGNIFICANCE HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.

Recovering Motor Activation with Chronic Peripheral Nerve Computer Interface

Interfaces with the peripheral nerve provide the ability to extract motor activation and restore sensation to amputee patients. The ability to chronically extract motor activations from the peripheral nervous system remains an unsolved problem. In this study, chronic recordings with the Flat Interface Nerve Electrode (FINE) are employed to recover the activation levels of innervated muscles. The FINEs were implanted on the sciatic nerves of canines, and neural recordings were obtained as the animal walked on a treadmill. During these trials, electromyograms (EMG) from the surrounding hamstring muscles were simultaneously recorded and the neural recordings are shown to be free of interference or crosstalk from these muscles. Using a novel Bayesian algorithm, the signals from individual fascicles were recovered and then compared to the corresponding target EMG of the lower limb. High correlation coefficients (0.84 ± 0.07 and 0.61 ± 0.12) between the extracted tibial fascicle/medial gastrocnemius and peroneal fascicle/tibialis anterior muscle were obtained. Analysis calculating the information transfer rate (ITR) from the muscle to the motor predictions yielded approximately 5 and 1 bit per second (bps) for the two sources. This method can predict motor signals from neural recordings and could be used to drive a prosthesis by interfacing with residual nerves.

A microfabricated platform for three-dimensional microsystems

This work presents a manually assembled three-dimensional (3-D) silicon platform structure that fully houses a microsystem containing sensors and the necessary system electronics including the power supply. The unique design of this platform provides a supporting package in a 3-D form factor, as well as routing capabilities with orthogonal mechanical and electrical connections between the assembled internal sides. An example platform is fabricated to realize an autonomous data-logging inertial measurement unit (IMU). The IMU consists of a programmable microcontroller, Flash memory, a three-axis accelerometer and a three-axis gyroscope. Double-sided polished, 250 µm-thick silicon wafers are processed using standard microfabrication techniques to produce the individual platform pieces. Sensor data, at a rate of 100Hz, is collected via a microcontroller and stored in the on-board Flash memory. Reliable and repeatable inertial data are collected with maximum power consumption of ∼16 mW, and the duration of data-logging capability of ∼48 hours. It is envisioned that extensions of this 3-D platform, combined with standard microfabrication techniques, will enable the integration of a variety of heterogeneous materials and devices with a form factor that reduces planar footprint and expands 3-D design space. Through-wafer vias can be used to integrate more functionality per surface area by increasing the interconnect density and allowing both the front and backside of the wafers to be populated with either fabricated or mounted devices.