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Cited by 138Analog and Mixed-Signal Circuit DesignNeuroscience and Neural EngineeringWireless Body Area Networks

The DOI matches its Crossref record; 108 of the 111 references it cites were found in a registry or an index.

Recent Advances in Neural Recording Microsystems

Open access
Gosselin, Benoit·2011·Sensors
DOI 10.3390/s110504572PMC PMC3231370
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Abstract

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

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Figures

Figure 1. Block diagrams showing different neural recording microsystem architectures. (a) This approach is employing analog TDM and is sharing a fast ADC between channels. (b) This approach is employing digital TDM and is using one ADC per channel. (c) This approach is employing analog TDM and is transferring digitization on the remote station side by using ATC in the implanted part, followed by TDC in the external part.
Figure 1. Block diagrams showing different neural recording microsystem architectures. (a) This approach is employing analog TDM and is sharing a fast ADC between channels. (b) This approach is employing digital TDM and is using one ADC per channel. (c) This approach is employing analog TDM and is transferring digitization on the remote station side by using ATC in the implanted part, followed by TDC in the external part.CC BY · PubMed Central
Figure 2. (a) Schematic of a capacitive-feedback neural amplifier. (b) Schematic of an active-feedback neural amplifier.
Figure 2. (a) Schematic of a capacitive-feedback neural amplifier. (b) Schematic of an active-feedback neural amplifier.CC BY · PubMed Central
Figure 3. (a) Current-mirror OTA. (b) Miller OTA. (c) Folded OTA with source degeneration. (d) Telescopic OTA. (e) Self-biased OTA.
Figure 3. (a) Current-mirror OTA. (b) Miller OTA. (c) Folded OTA with source degeneration. (d) Telescopic OTA. (e) Self-biased OTA.CC BY · PubMed Central
Figure 4. A data reduction scheme employing a bilateral threshold function. In such scheme, action potentials are detected upon threshold crossing and then tagged with their respective time of occurrence.
Figure 4. A data reduction scheme employing a bilateral threshold function. In such scheme, action potentials are detected upon threshold crossing and then tagged with their respective time of occurrence.CC BY · PubMed Central
Figure 5. Block diagram of a signal detector employing a pre-processor.
Figure 5. Block diagram of a signal detector employing a pre-processor.CC BY · PubMed Central
Figure 6. Block diagram of an implantable neural recording microsystem and its multi-carrier wireless interface.
Figure 6. Block diagram of an implantable neural recording microsystem and its multi-carrier wireless interface.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Kandel, ER, Schwartz, JH, and Jessell, TM (2000). Principles of Neural Science, McGraw-Hill Medical.

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Metadata

Authors
Gosselin, B.
Affiliations
Université Laval (CA)
Corresponding author
Gosselin, Benoit
ORCID
Gosselin, Benoit
Venue
Sensors
Publisher
MDPI AG
Published
2011-04-27
Volume
11
Issue
5
Pages
4572-4597
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s110504572
PMC
PMC3231370
OpenAlex
W2153710474
Cited by
138
References
111
Citations by year
2026: 4 · 2025: 2 · 2024: 7 · 2023: 7 · 2022: 10 · 2021: 9 · 2020: 8 · 2019: 5 · 2018: 9 · 2017: 12 · 2016: 12 · 2015: 21 · 2014: 19 · 2013: 5 · 2012: 7
FWCI
0.82
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • CMC Microsystems
  • Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
Keywords
neural interfaces, multichannel neural recording, neural probes, low-power circuits, data management, brain-computer interface, neural prostheses
MeSH terms
Equipment Design, Humans, Neurons, Telemetry, Biosensing Techniques

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