Design of a low-noise preamplifier for nerve cuff electrode recording
Abstract
This paper discusses certain important issues involved in the design of a nerve signal preamplifier for implantable neuroprostheses. Since the electroneurogram signal measured from cuff electrodes is typically on the order of 1 μV, a very low-noise interface is essential. We present the argument for the use of BiCMOS technology in this application and then describe the design and evaluation of a complete preamplifier fabricated in a 0.8-μm double-metal double-poly process. The preamplifier has a nominal voltage gain of 100, a bandwidth of 15 kHz, and a measured equivalent input-referred noise voltage spectral density of 3.3 nV//spl radic/Hz at 1 kHz. The total input-referred rms noise voltage in a bandwidth 1 Hz-10 kHz is 290 nV, the power consumption is 1.3 mW from ±2.5-V power supplies, and the active area is 0.3 mm2.
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Amplifier noise is typically designed to be lower than electrode noise and is maintained below 5 μVrms for CNS-recording amplifiers and below 1 μVrms for PNS-recording amplifiers.
“The noise introduced by the amplifier is typically designed to be lower than that introduced by the electrodes and, for a majority of neural applications, is maintained below 5 µVrms for amplifiers used to record CNS signals and less than 1 µVrms for PNS-recording amplifiers [76, 105].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [105]
CNS-based neuroprostheses, or brain-machine interfaces, decode neural electrical activity to control robotic limbs or to stimulate muscles for restoring limb function.
“CNS-based neuroprostheses, or brain–machine interfaces (BMIs), are systems whereby neural electrical activities are decoded and used to control robotic limbs [76, 89, 105] or to stimulate muscles for restoring limb function [35].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [105]
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Self-citation
- Authors
- Rieger, R., Taylor, J., Demosthenous, A., Donaldson, N., & Langlois, P. J.
- Affiliations
- University College London (GB)
- University of Bath (GB)
- Venue
- IEEE Journal of Solid-State Circuits
- Published
- 2003-08
- Volume
- 38
- Issue
- 8
- Pages
- 1373-1379
- ISSN
- 0018-9200 (ISSN-L)
- OpenAlex
- W2059054388
- Cited by
- 78
- References
- 20
- Citations by year
- 2026: 1 · 2024: 3 · 2022: 1 · 2021: 1 · 2020: 7 · 2019: 1 · 2018: 2 · 2017: 3 · 2016: 4 · 2014: 5 · 2013: 3 · 2012: 2
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Solid-State Circuits (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radio Frequency Integrated Circuit Design
- Keywords
- low-noise preamplifier, cuff electrodes, electroneurogram, BiCMOS, input-referred noise, neural recording, low-power analog circuits
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