Recent Advances in Neural Recording Microsystems
Open accessAbstract
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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In the dc-coupled topology, the input offsets are removed via an active low-pass filter in the feedback path, forming a Miller integrator and avoiding the need for large input capacitors, which saves considerable area.
“In the dc-coupled topology shown in Fig. 6(b), the input offsets are removed via an active low-pass filter in the feedback path, forming a Miller integrator and avoiding the need for large input capacitors, which saves considerable area [159], [172].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [159]
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- 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)
- 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
- gold
- doi.org
- 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 › Biomedical 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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