Design and Testing of Stimulation and Myoelectric Recording Modules in an Implanted Distributed Neuroprosthetic System

Abstract

Implantable motor neuroprostheses can restore functionality to individuals with neurological disabilities by electrically activating paralyzed muscles in coordinated patterns. The typical design of neuroprosthetic systems relies on a single multi-use device, but this limits the number of stimulus and sensor channels that can be practically implemented. To address this limitation, a modular neuroprosthesis, the "Networked Neuroprosthesis" (NNP), was developed. The NNP system is the first fully implanted modular neuroprosthesis that includes implantation of all power, signal processing, biopotential signal recording, and stimulating components. This paper describes the design of stimulation and recording modules, bench testing to verify stimulus outputs and appropriate filtering and recording, and validation that the components function properly while implemented in persons with spinal cord injury. The results of system testing demonstrated that the NNP was functional and capable of generating stimulus pulses and recording myoelectric, temperature, and accelerometer signals. Based on the successful design, manufacturing, and testing of the NNP System, multiple clinical applications are anticipated.

Figures

Fig. 1:

Potential NNP multi-function configuration for hand and trunk function. (PM – power module, PG4 – pulse generator module, BP2 – biopotential recording module)

Fig. 2:

Images of remote modules with attached cabling. a) PG4 module, b) BP2 module. Red cables are plugged into stimulus output ports, green cables are plugged into recording input ports, and blue cables are plugged into network ports. Capped network connections are unused.

Fig. 3:

Remote module circuit. a) PG4 flex-circuit unfolded, b) illustration of folding process, and c) folded module in titanium enclosure.

Fig. 4:

Block diagram of PG4

Fig. 5:

Stimulus circuitry with the elements and path shown for a single channel. Text with lines on the left hand side indicate the function and transmitted information to/from the microcontroller. ‘Compliance detect’ is an input to the microcontroller while the remaining signals are outputs to control pulse generation circuitry

Fig. 6:

Block diagram of BP2

Fig. 7:

Traces showing the network physical layer running at 9.5V shown at different resolutions. a) Five pairs of pulses comprising a single CAN bit. b) Superimposed view of a single dominant and recessive network cycle. The data were recorded separately from the top plot but demonstrate a zoomed-in representation of the type of data in the top plot. c) Round trip delay of a remote module CAN controller transmitting a bit and receiving the same bit back from the network.

Fig. 8:

Benchtop stimulus responses from the PG4 across a resistor: a) PW = 250μs, PA = 4, 8, 12, 16, 20mA, R=500 Ω, b) PW = 50, 100, 150, 200, 250μs, PA = 10mA, R=500 Ω c) PW=250μs, PA = 10, 5, 2.5mA, R=250, 500, 1000 Ω

Fig. 9:

Benchtop recording from a function generator with the BP2: a) Filter output relative to a 1mV P-P input at varied frequencies, Gain = 1500. Blue dots indicate recorded responses at a given frequency, the black line represents the modeled representation of the circuit, the yellow band shows the frequency range from 20–200Hz. b) Input: varied amplitudes at 100Hz, Gain = 1500.

Fig. 10:

Power consumption by the PG4 and BP2 Remote Module at varying network voltages (5.5V-9.5V) and activity types: 1) Inactive, 2) Idle, 3) Minimum Stimulation, 4) Maximum Stimulation, and 5) Active Recording.

Fig. 11:

Demonstration of grasp postures: a) pinch grasp, b) power grip

Fig. 12:

EMG recordings in an implant recipient. a) voluntary effort recorded from ECRL (gain=195) without stimulation, b) stimulus artifact recorded from ECRL (gain=195) during stimulation applied to APL-EDC (tendon-transferred muscle) while user was relaxed (no voluntary effort), c) stimulus artifact recorded from ECRL (gain=195) during stimulation to gluteus maximus while user was relaxed, d) voluntary effort and artifact recorded from brachioradialis (gain=1500) while stimulation was applied to ECU, e) blanking applied to extract myoelectric command signal from (d) (14ms extracted per frame), f) smaller window of the signals from (d) (red) and (e) (blue), and g) waveform length extracted from blanked myoelectric signal in (e) and scaled to 0–255.

Fig. 13:

Accelerometer data from a module in abdomen demonstrating measurement during trunk tilt. The Z-axis is directed medially and posteriorly (into the page).