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Cited by 67Analog and Mixed-Signal Circuit DesignNeuroscience and Neural EngineeringAdvanced Memory and Neural Computing

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

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Zhu, Bingzhao, Shin, Uisub, Shoaran, Mahsa·2021·IEEE Transactions on Biomedical Circuits and Systems
DOI 10.1109/tbcas.2021.3112756PMC PMC8733782
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Abstract

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

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  • An important challenge in real time neural decoding involves balancing the accuracy-latency trade-off, which requires careful consideration according to the system application.

    “An important challenge in real time neural decoding involves balancing the accuracy-latency trade-off, which requires careful consideration according to the system application [201].”

    From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [201]

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Research works printed but not linked: 6

  1. [56]Johnson BC, Gambini S, Izyumin I, Moin A, Zhou A, Alexandrov G, Santacruz SR, Rabaey JM, Carmena JM, and Muller R, “ An implantable 700μw 64-channel neuromodulation ic for simultaneous recording and stimulation with rapid artifact recovery,” in 2017 Symposium on VLSI Circuits. IEEE, 2017, pp. C48– C49.
  2. [58]Chandrakumar H and Marković D, “ An 80-mvpp linear-input range, 1.6-g-ohm input impedance, low-power chopper amplifier for closed-loop neural recording that is tolerant to 650-mvpp common-mode interference,” IEEE journal of solid-state circuits, vol. 52, no. 11, pp. 2811– 2828, 2017.
  3. [72]Shoaran M, Shahshahani M, Farivar M, Almajano J, Shahshahani A, Schmid A, Bragin A, Leblebici Y, and Emami A, “ A 16-channel 1.1 mm 2 implantable seizure control soc with sub-μw/channel consumption and closed-loop stimulation in 0.18 μm cmos,” in 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits). Ieee, 2016, pp. 1– 2.
  4. [73]Altaf MAB, Tillak J, Kifle Y, and Yoo J, “ A 1.83 μJ/classification nonlinear support-vector-machine-based patient-specific seizure classification SoC,” in 2013 ISSCC. IEEE, 2013, pp. 100– 101.
  5. [92]Shoaran M, Haghi BA, Farivar M, and Emami A, “ Efficient feature extraction and classificatin methods in neural interfaces,” in Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2017 Symposium, vol. 47, no. 4, 2017, pp. 31– 35.
  6. [100]Aslam AR, Iqbal T, Aftab M, Saadeh W, and Altaf MAB, “ A 10.13μJ/classification 2-channel deep neural network-based SoC for emotion detection of autistic children,” in 2020 IEEE Custom Integrated Circuits Conference (CICC). IEEE, 2020, pp. 1– 4.

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Authors
Zhu, B., Shin, U., & Shoaran, M.
Affiliations
  • Cornell University (US)
  • École Polytechnique Fédérale de Lausanne (CH)
ORCID
Venue
IEEE Transactions on Biomedical Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-10
Volume
15
Issue
5
Pages
877-897
ISSN
  • 1932-4545 (ISSN-L)
  • 1940-9990
DOI
10.1109/tbcas.2021.3112756
PMC
PMC8733782
OpenAlex
W3199832236
Cited by
67
References
132
Citations by year
2026: 12 · 2025: 16 · 2024: 14 · 2023: 13 · 2022: 10 · 2021: 1 · 2019: 1
FWCI
0.80
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (cc-by)
Funders
National Institute of Mental Health (R01-MH-123634)
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
application-specific integrated circuits, neuromodulation devices, tree-based classifiers, oblique decision trees, ensemble methods, algorithm-hardware co-design, low-latency classification, seizure detection, multichannel neural recording
MeSH terms
Machine Learning, Brain, Intelligence, Signal Processing, Computer-Assisted, Neural Prostheses

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