Evidence map›Paper›PMID 40433949›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Soft Bioelectronic Interfaces for Continuous Peripheral Neural Signal Recording and Robust Cross-Subject Decoding.

Liangpeng Chen, Yang Liu, Yewei Huang, Ziyang Li, Chao Zhang, Jun Tang, Xueyuan Ling, Bowen Cao, Baowang Li, Yuan Zhang and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Liangpeng ChenDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Yang LiuDepartment of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
Yewei HuangDepartment of Materials Science and Engineering, University of Pennsylvania, Philadelphia, 19104, USA.
Ziyang LiDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Chao ZhangDepartment of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
Jun TangDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Xueyuan LingDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Bowen CaoDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Baowang LiDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Yuan ZhangDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Wenjianlong ZhouDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Qin XuDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Shunchang MaDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Xiudong GuanDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Dan XiaoChina National Clinical Research Center for Neurological Diseases (NCRC-ND), Beijing, 100070, China.
Jingyao GengChina National Clinical Research Center for Neurological Diseases (NCRC-ND), Beijing, 100070, China.
Yutong ZhaoBeijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, China.
Guolin LiDepartment of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
Yi-Xuan WangTianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, 300072, China.
Wang JiaDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.
Yuanwen JiangDepartment of Materials Science and Engineering, University of Pennsylvania, Philadelphia, 19104, USA.
Milin ZhangDepartment of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
Deling LiDepartment of Neurosurgery, Beijing Tiantan Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing, 100070, China.ORCID https://orcid.org/0000-0002-4300-7136

Funding

Beijing Municipal Natural Science Foundation Z220015Beijing Youth Scholar Program 053National Natural Science Foundation of China 92164202National Outstanding Youth Science Fund Project of National Natural Science Foundation of China 82222034Outstanding Youth Science Fund Project of Capital Medical University
6 · The paper itself

Abstract

Accurate decoding of peripheral nerve signals is essential for advancing neuroscience research, developing therapeutics for neurological disorders, and creating reliable human-machine interfaces. However, the poor mechanical compliance of conventional metal electrodes and limited generalization of existing decoding models have significantly hindered progress in understanding peripheral nerve function. This study introduces low-impedance, soft-conducting polymer electrodes capable of forming stable, intimate contacts with peripheral nerve tissues, allowing for continuous and reliable recording of neural activity in awake animals. Using this unique dataset of neurophysiological signals, a neural network model that integrates both handcrafted and deep learning-derived features, while incorporating parameter-sharing and adaptation training strategies, is developed. This approach significantly improves the generalizability of the decoding model across subjects, reducing the reliance on extensive training data. The findings not only deepen the understanding of peripheral nerve function but also open avenues for developing advanced interventions to augment and restore neurological functions.

Indexed as

Peripheral NervesAnimalsDeep LearningElectrodes, ImplantedHumansNeural Networks, Computercross‐subject decodingneural network decodingPEDOT:PSSperipheral neural signal recordingsoft bioelectronic interface

Identifiers

PMID40433949
PMCPMC12412577

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.