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Adhesive and Hydrophobic Bilayer Hydrogel Enabled On‐Skin Biosensors for High‐Fidelity Classification of Human Emotion.

Authors :
Yang, Ganguang
Zhu, Kanhao
Guo, Wei
Wu, Dongrui
Quan, Xueliang
Huang, Xin
Liu, Shaoyu
Li, Yangyang
Fang, Han
Qiu, Yuqi
Zheng, Qingyang
Zhu, Mengliang
Huang, Jian
Zeng, Zhigang
Yin, Zhouping
Wu, Hao
Source :
Advanced Functional Materials; 7/18/2022, Vol. 32 Issue 29, p1-16, 16p
Publication Year :
2022

Abstract

Traditional human emotion recognition is based on electroencephalogram (EEG) data collection technologies which rely on plenty of rigid electrodes and lack anti‐interference, wearing comfort, and portability. Moreover, a significant distribution difference in EEG data also results in low classification accuracy. Here, on‐skin biosensors with adhesive and hydrophobic bilayer hydrogel (AHBH) as interfaces for high accuracy emotion classification are proposed. The AHBH achieves remarkable adhesion (59.7 N m−1) by combining the adhesion mechanism of catechol groups and electrostatic attraction. Meanwhile, based on the synergistic effects of hydrophobic group rearrangements and surface energy reduction, the AHB‐hydrophobic layer exhibits 133.87° water contact angles through hydrophobic treatment of only 0.5 h. Hydrogen and electrostatic bonds are also introduced to form a seamless adhesive‐hydrophobic hydrogel interface and inhibit adhesion attenuation, respectively. With the AHBH as an ideal device/skin interface, the biosensor can reliably collect high‐quality electrophysiological signals even under vibration, sweating, and long‐lasting monitoring condition. Furthermore, the on‐skin electrodes, data processing, and wireless modules are integrated into a portable headband for EEG‐based emotion classification. A domain adaptive neural network based on the transfer learning technique is introduced to alleviate the effect of domain shift and achieve high classification accuracy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1616301X
Volume :
32
Issue :
29
Database :
Complementary Index
Journal :
Advanced Functional Materials
Publication Type :
Academic Journal
Accession number :
158043169
Full Text :
https://doi.org/10.1002/adfm.202200457