Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief
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Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.
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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 [172]
The Neural Recording Amplifiers section (pp. 2–3) describes a DC-coupled amplifier that removes input DC offset using an active low-pass integrator in the feedback path. It says this architecture does not require a large capacitor and explains that large input capacitors occupy significant chip area. Our copy does not identify the integrator as a Miller integrator. (LLM, conf=0.83)
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Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review
Habibollahi, M., Jiang, D., Lancashire, H. T., & Demosthenous, A. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3430038 · Cited by 11 · see citing works
“Pre-processing techniques such as compressive sensing and adaptive sampling enable efficient digitisation by reducing the amount of recorded data to the biosignals using thresholding methods, thus taking advantage of the sparsity of neural signals in the time or frequency domains [172].”
- Authors
- Chen, J., Tarkhan, M., Wu, H., Noshahr, F. H., Yang, J., & Sawan, M.
- Affiliations
- Westlake University (CN)
- Zhejiang University (CN)
- Venue
- IEEE Transactions on Circuits and Systems II: Express Briefs
- Published
- 2022-06
- Volume
- 69
- Issue
- 6
- Pages
- 2654-2660
- ISSN
- 1549-7747 (ISSN-L)
- 1558-3791
- OpenAlex
- W4281720573
- Cited by
- 38
- References
- 72
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 10 · 2023: 12 · 2022: 6
- FWCI
- 2.61
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Circuits & Systems II Express Briefs (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Zhejiang Key Research and Development Program (2021C03002)
- Zhejiang Leading Innovative and Entrepreneur Team Introduction (2020R01005)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- neural recording system, system-level specification, noise reduction, closed-loop neuromodulation, neurodegenerative diseases, mental illness
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