EARNEST is a complete embedded system for neural prosthetic applications. It includes recording capabilities of electroneurographic (ENG) signals from up to 4 intrafascicular electrodes with 16 channels each, recording of electromyographic(EMG) signals from 4 differential surface electrodes and multi-channel programmable electrical stimulation. It realizes a complete system for closed-loop bidirectional communication between the Peripheral Neural System (PNS) and an artificial limb. The system is built upon a programmable core for System-on-Chip and 3 different application specific integrated circuits (ASIC) realized in a 0.35μ-m CMOS high-voltage process from ams designed to meet the constraints in terms of area, noise and power in view of a fully implantable system. The whole system has been successfully tested by means of in-vivo experiments with animal models.

EARNEST: A 64 channel device for neural recording and sensory touch restoration in neural prosthetics

Carboni, Caterina;Bisoni, Lorenzo;Pani, Danilo;Raffo, Luigi;Barbaro, Massimo
2018-01-01

Abstract

EARNEST is a complete embedded system for neural prosthetic applications. It includes recording capabilities of electroneurographic (ENG) signals from up to 4 intrafascicular electrodes with 16 channels each, recording of electromyographic(EMG) signals from 4 differential surface electrodes and multi-channel programmable electrical stimulation. It realizes a complete system for closed-loop bidirectional communication between the Peripheral Neural System (PNS) and an artificial limb. The system is built upon a programmable core for System-on-Chip and 3 different application specific integrated circuits (ASIC) realized in a 0.35μ-m CMOS high-voltage process from ams designed to meet the constraints in terms of area, noise and power in view of a fully implantable system. The whole system has been successfully tested by means of in-vivo experiments with animal models.
2018
9781509058037
Biomedical Engineering; Electrical and Electronic Engineering; Instrumentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/257497
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