Automated stimulus-response mapping of high-electrode-count neural implants

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Technology

Background:

• High-electrode-count (HEC) devices offer advanced neural population access for functional electrical stimulation (FES).
• Current FES applications are limited by the absence of suitable hardware and software for HEC devices.
• Efficient calibration of HEC neural interfaces is crucial for research and clinical translation.

Purpose of the Study:

• To introduce the first generation of a closed-loop FES platform specifically designed for HEC neural interface devices.
• To develop and validate automated software routines for mapping stimulus-response properties of HEC devices.
• To demonstrate the platform's capability in automating time-intensive calibration procedures.

Main Methods:

• A closed-loop FES platform was developed, featuring a 1100-channel stimulator, biometric devices, and a 160-channel data recorder.
• Two automated software routines were implemented: one for perithreshold muscle activity mapping and another for recruitment curve generation.
• The platform and routines were tested using 100-electrode Utah Slanted Electrode Arrays (USEAs) in cat hindlimb nerves, measuring joint torque and EMG.

Main Results:

• Automated mapping of perithreshold stimulus levels averaged 16.4s for responsive electrodes and 3.6s for non-responsive electrodes.
• Locating recruitment curve asymptotes averaged 9.6s per electrode, with each recruitment curve point taking 0.87s.
• The platform successfully automated the typically time- and effort-intensive stimulus-response mapping process.

Conclusions:

• The presented closed-loop FES platform effectively supports HEC neural interface devices.
• Automated stimulus-response mapping routines significantly enhance the efficiency of HEC device calibration.
• This platform facilitates the broader adoption of HEC devices in research and clinical settings.