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

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

Highlights 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 respon...

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Bibliographic Details
Main Authors: Kai Zheng, Chengcheng Zheng, Lixian Zhu, Bihai Yang, Xiaokun Jin, Su Wang, Zikai Song, Jingyu Liu, Yan Xiong, Fuze Tian, Ran Cai, Bin Hu
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Nano-Micro Letters
Subjects:
Entangled network
Reusable adhesion
Epidermal sensor
Machine learning
Attention assessment
Online Access:https://doi.org/10.1007/s40820-025-01780-7
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Summary:Highlights 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.
ISSN:2311-6706
2150-5551

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