A Highly Selective 3D Spiked Ultraflexible Neural (SUN) Interface for Decoding Peripheral Nerve Sensory Information
Abstract
Artificial sensors on the skin are proposed as a way to capture information that can be used in intracortical microstimulation or peripheral intraneural stimulation to restore sensory feedback to persons with tetraplegia. However, the ability of these artificial sensors to replicate the density and complexity of the natural mechanoreceptors is limited. One relatively unexplored approach is to make use of the signals from surviving tactile and proprioceptive receptors in existing limbs by recording from their transmitting axons within the primary sensory nerves. Here, a novel spiked ultraflexible neural (SUN) interface that is implanted into the peripheral nervous system to capture sensory information from these mechanoreceptors in acute rat experiments is described. The novel 3D design, which integrates spiked structures for intrafascicular nerve recording with an ultraflexible substrate, enables a unique conformal interface to the target nerve. With the high‐quality recording (average signal‐to‐noise‐ratio of 1.4) provided by the electrode, tactile from proprioceptive stimuli can be differentiated in terms of the firing rate. In toe pinching experiments, high spatial resolution classification can be achieved with support vector machine classifier. Further work remains to be done to assess the chronic recording capability of the SUN interface.
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The SUN interface enabled deformation following nerve movements and penetrating spiked electrodes for intra-fascicular recordings.
“This enabled the SUN interface to deform following nerve movements and penetrating spiked electrodes for intra-fascicular recordings.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [247]
In vivo experiments on the rat sciatic nerve detected small amplitude electroneurogram (ENG) signals with a high signal-to-noise ratio (SNR).
“They performed in vivo experiments on the rat sciatic nerve to detect small amplitude electroneurogram (ENG) signals with a high signal-to-noise ratio (SNR).”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [247]
The SUN interface could differentiate tactile from proprioceptive stimuli and classify the location of the stimulus with high spatial resolution.
“They could differentiate tactile from proprioceptive stimuli and classify the location of the stimulus with a high spatial resolution (Figure 7f).”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [247]
Wang et al. fabricated a spiked ultra-flexible neural (SUN) interface for recording from the peripheral nervous system.
“Wang et al. [247] fabricated a spiked ultra-flexible neural (SUN) interface for recording from the peripheral nervous system.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [247]
Advanced neuroprostheses require neural interfaces capable of high quality recording from efferent signals to record intention of movements and reliable stimulation to provide sensory feedback to the brain.
“For instance, advanced neuroprostheses require neural interfaces capable of the high quality of recording from efferent signals to record intention of any movements as well as capable of reliable stimulation to provide sensory feedback to the brain.99,100”
From From flexible electronics technology in the era of IoT and artificial intelligence toward future implanted body sensor networks (2019) · cites this paper as [100]
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- Authors
- Wang, J., Thow, X. Y., Wang, H., Lee, S., Voges, K., Thakor, N. V., Yen, S., & Lee, C.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Thakor, Nitish V.
- Yen, Shih‐Cheng
- Lee, Chengkuo
- ORCID
- Venue
- Advanced Healthcare Materials
- Publisher
- Wiley
- Published
- 2017-12-04
- Volume
- 7
- Issue
- 5
- ISSN
- 2192-2640 (ISSN-L)
- 2192-2659
- OpenAlex
- W2774278772
- Semantic Scholar
- 41548a6aae3757b9c7058f283baabca49a63e768
- Cited by
- 43
- References
- 41
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 7 · 2022: 7 · 2021: 6 · 2020: 6 · 2019: 6 · 2018: 3
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advanced Healthcare Materials (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Research Foundation Singapore (NRF‐CRP10‐2012‐01)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- peripheral nerve recording, mechanoreceptors, tetraplegia, intracortical microstimulation, signal-to-noise ratio
- MeSH terms
- Animals, Axons, Electrodes, Implanted, Mechanoreceptors, Synaptic Transmission, Peripheral Nerves, Proprioception, Rats, Sprague-Dawley, Rats
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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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