Machine Learning Enabled Reusable Adhesion, Entangled Network-Based Hydrogel for Long-Term, High-Fidelity EEG Recording and Attention Assessment

Kai Zheng; Chengcheng Zheng; Lixian Zhu; Bihai Yang; Xiaokun Jin; Su Wang; Zikai Song; Jingyu Liu; Yan Xiong; Fuze Tian; Ran Cai; Bin Hu

doi:10.1007/s40820-025-01780-7

Machine Learning Enabled Reusable Adhesion, Entangled Network-Based Hydrogel for Long-Term, High-Fidelity EEG Recording and Attention Assessment

Kai Zheng, Chengcheng Zheng, Lixian Zhu, Bihai Yang, Xiaokun Jin, Su Wang, Zikai Song, Jingyu Liu, Yan Xiong, Fuze Tian^*, Ran Cai^*, Bin Hu^*

^*此作品的通讯作者

医学技术学院

科研成果: 期刊稿件 › 文章 › 同行评审

5 引用（Scopus）

摘要

A dual-network hydrogel (PGEH) cross-linked via liquid metal induction was developed exhibiting remarkable mechanical properties and skin-temperature-triggered on-demand adhesion capabilities. The PGEH capacitive sensor demonstrates exceptional sensitivity (1.25 kPa), rapid dynamic response (30 ms), and long-term cycling stability (20,000 cycles), enabling precise monitoring of human motion and reliable signal transmission. Low-impedance electrophysiological sensor (310 ohms) maintains 14-day signal fidelity (25.2 dB), paired with machine learning-based attention monitoring (91.38% of accuracy) for real-time cognitive feedback in focus-demanding scenarios.

源语言	英语
文章编号	281
期刊	Nano-Micro Letters
卷	17
期	1
DOI	http://doi.org/10.1007/s40820-025-01780-7
出版状态	已出版 - 12月 2025

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title = "Machine Learning Enabled Reusable Adhesion, Entangled Network-Based Hydrogel for Long-Term, High-Fidelity EEG Recording and Attention Assessment",

abstract = "A dual-network hydrogel (PGEH) cross-linked via liquid metal induction was developed exhibiting remarkable mechanical properties and skin-temperature-triggered on-demand adhesion capabilities. The PGEH capacitive sensor demonstrates exceptional sensitivity (1.25 kPa), rapid dynamic response (30 ms), and long-term cycling stability (20,000 cycles), enabling precise monitoring of human motion and reliable signal transmission. Low-impedance electrophysiological sensor (310 ohms) maintains 14-day signal fidelity (25.2 dB), paired with machine learning-based attention monitoring (91.38\% of accuracy) for real-time cognitive feedback in focus-demanding scenarios.",

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author = "Kai Zheng and Chengcheng Zheng and Lixian Zhu and Bihai Yang and Xiaokun Jin and Su Wang and Zikai Song and Jingyu Liu and Yan Xiong and Fuze Tian and Ran Cai and Bin Hu",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",

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doi = "10.1007/s40820-025-01780-7",

language = "English",

volume = "17",

journal = "Nano-Micro Letters",

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AU - Zheng, Kai

AU - Zheng, Chengcheng

AU - Zhu, Lixian

AU - Yang, Bihai

AU - Jin, Xiaokun

AU - Wang, Su

AU - Song, Zikai

AU - Liu, Jingyu

AU - Xiong, Yan

AU - Tian, Fuze

AU - Cai, Ran

AU - Hu, Bin

PY - 2025/12

Y1 - 2025/12

N2 - A dual-network hydrogel (PGEH) cross-linked via liquid metal induction was developed exhibiting remarkable mechanical properties and skin-temperature-triggered on-demand adhesion capabilities. The PGEH capacitive sensor demonstrates exceptional sensitivity (1.25 kPa), rapid dynamic response (30 ms), and long-term cycling stability (20,000 cycles), enabling precise monitoring of human motion and reliable signal transmission. Low-impedance electrophysiological sensor (310 ohms) maintains 14-day signal fidelity (25.2 dB), paired with machine learning-based attention monitoring (91.38% of accuracy) for real-time cognitive feedback in focus-demanding scenarios.

AB - A dual-network hydrogel (PGEH) cross-linked via liquid metal induction was developed exhibiting remarkable mechanical properties and skin-temperature-triggered on-demand adhesion capabilities. The PGEH capacitive sensor demonstrates exceptional sensitivity (1.25 kPa), rapid dynamic response (30 ms), and long-term cycling stability (20,000 cycles), enabling precise monitoring of human motion and reliable signal transmission. Low-impedance electrophysiological sensor (310 ohms) maintains 14-day signal fidelity (25.2 dB), paired with machine learning-based attention monitoring (91.38% of accuracy) for real-time cognitive feedback in focus-demanding scenarios.

KW - Attention assessment

KW - Entangled network

KW - Epidermal sensor

KW - Machine learning

KW - Reusable adhesion

UR - http://www.scopus.com/pages/publications/105006885678

U2 - 10.1007/s40820-025-01780-7

DO - 10.1007/s40820-025-01780-7

M3 - Article

AN - SCOPUS:105006885678

SN - 2311-6706

VL - 17

JO - Nano-Micro Letters

JF - Nano-Micro Letters

IS - 1

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ER -

Machine Learning Enabled Reusable Adhesion, Entangled Network-Based Hydrogel for Long-Term, High-Fidelity EEG Recording and Attention Assessment

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