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

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.

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Publication Details

Journal
Advanced Science
Published
2025-05-28
DOI
https://doi.org/10.1002/advs.202414732
Citations
1
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.91

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article

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

Xuxinyi Ling, Jingyao Geng, Jun Tang, Liangpeng Chen et al.
1 citations
Advanced Science
Neuroscience and Neural Engineering
0.91
article

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

Xuxinyi Ling, Jingyao Geng, Jun Tang, Liangpeng Chen, Baowang Li, Xiudong Guan, Qin Xu, Yutong Zhao, Bowen Cao, Yewei Huang, Yuanwen Jiang, Yang Liu, Deling Li, Yixuan Wang, Chao Zhang, Shunchang Ma, Milin Zhang, Ziyang Li, Dan Xiao, Guolin Li, Yuan Zhang, Wenjianlong Zhou, Jia Wang
article en
1 citations

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.

Advanced ScienceVol. 12(33)
Tianjin University (CN), Capital Medical University (CN), National Clinical Research Center for Digestive Diseases (CN), Beijing Institute of Neurosurgery (CN), University of Pennsylvania (US), Tsinghua University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
No poverty
Openalex Percentile: Top 27%
Neuroscience and Neural Engineering
0.91
DOIOpen Access PDFOpenAlex
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