A Wide Dynamic Range Neural Data Acquisition System with High-Precision Delta-Sigma ADC and On-Chip EC-PC Spike Processor
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A high-performance, wide dynamic range, fully-integrated neural interface is one key component for many advanced bidirectional neuromodulation technologies. In this paper, to complement the previously proposed frequency-shaping amplifier (FSA) and high-precision electrical microstimulator, we will present a proof-of-concept design of a neural data acquisition (DAQ) system that includes a 15-bit, low-power Delta-Sigma analog-to-digital converter (ADC) and a real-time spike processor based on one exponential component-polynomial component (EC-PC) algorithm. High-precision data conversion with low power consumption and small chip area is achieved by employing several techniques, such as opamp-sharing, multi-bit successive approximation (SAR) quantizer, two-step summation, and ultra-low distortion data weighted averaging (DWA). The on-chip EC-PC engine enables low latency, automatic detection, and extraction of spiking activities, thus supporting closed-loop control, real-time data compression and /or neural information decoding. The prototype chip was fabricated in a 0.13 μm CMOS process and verified in both bench-top and In-Vivo experiments. Bench-top measurement results indicate the designed ADC achieves a peak signal-to-noise and distortion ratio (SNDR) of 91.8 dB and a dynamic range of 93.0 dB over a 10 kHz bandwidth, where the total power consumption of the modulator is only 20 μW at 1.0 V supply, corresponding to a figure-of-merit (FOM) of 31.4fJ /conversion-step. In In-Vivo experiments, the proposed DAQ system has been demonstrated to obtain high-quality neural activities from a rat's motor cortex and also greatly reduce recovery time from system saturation due to electrical microstimulation.
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Techniques such as a wide input dynamic range reduce the risk of front-end saturation and prevent loss of data.
“Techniques such as a wide input dynamic range reduce the risk of front-end saturation and prevent loss of data [161].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [161]
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- Authors
- Xu, J., Nguyen, A. T., Wu, T., Zhao, W., Luu, D. K., & Yang, Z.
- Affiliations
- University of Minnesota (US)
- Binghamton University (US)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2020
- Volume
- 14
- Issue
- 3
- Pages
- 1-1
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- PMC
- PMC7310583
- OpenAlex
- W3005178570
- Cited by
- 29
- References
- 47
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 5 · 2020: 3
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health (1R21NS111214-01, 5R01MH111413-04)
- 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 › Advanced Memory and Neural Computing
- Keywords
- sigma-delta ADC, neural interfaces, neuromodulation, low-power analog-to-digital converter, data weighted averaging, opamp sharing, spike detection, feedback control, electrical microstimulation
- MeSH terms
- Action Potentials, Algorithms, Analog-Digital Conversion, Animals, Brain, Transcutaneous Electric Nerve Stimulation, Equipment Design, Rats, Sprague-Dawley, Data Compression, Rats
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