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Zhang et al. Soft Sci 2024;4:39 Soft Science
DOI: 10.20517/ss.2024.34

Review Article Open Access

Recent progress of hydrogels in brain-machine

interface

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Mingxuan Zhang 1,2,# , Mingming Hao 3,#,* , Botao Liu , Jianping Chen , Guoqiang Ren , Yinchao Zhao , Jinxiu
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Guo , Liping Zhuang , Shunying Zhao , Zhaoxiang Peng , Jiangfang Lian , Jingjin Wu , Yi Chen , Jingyun
3,*
Ma , Qifeng Lu 1,*
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School of CHIPS, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Taicang 215400, Jiangsu, China.
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Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK.
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The Affiliated Lihuili Hospital of Ningbo University, Ningbo 315046, Zhejiang, China.
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School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, Hainan, China.
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School of Intelligent Manufacturing and Ecosystems, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool
University, Taicang 215400, Jiangsu, China.
#
Authors contributed equally.
* Correspondence to: Dr. Mingming Hao, Prof. Jingyun Ma, The Affiliated Lihuili Hospital of Ningbo University, Ningbo 315046,
Zhejiang, China. E-mail: 18306213728@163.com; majingyun198401@126.com; Dr. Qifeng Lu, School of CHIPS, XJTLU
Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Taicang 215400, Jiangsu, China. E-mail:
qifeng.lu@xjtlu.edu.cn
How to cite this article: Zhang M, Hao M, Liu B, Chen J, Ren G, Zhao Y, Guo J, Zhuang L, Zhao S, Peng Z, Lian J, Wu J, Chen Y,
Ma J, Lu Q. Recent progress of hydrogels in brain-machine interface. Soft Sci 2024;4:39. https://dx.doi.org/10.20517/ss.2024.
34
Received: 27 Aug 2024 First Decision: 11 Oct 2024 Revised: 13 Nov 2024 Accepted: 15 Nov 2024 Published: 27 Nov 2024
Academic Editor: Xinge YU Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang
Abstract
The long-term stable monitoring of brain signals, including electroencephalogram (EEG), electrocorticogram
(ECoG) and local field potential (LFP), is of great significance for the fundamental research in brain science,
artificial intelligence and the diagnosis and treatment of brain-related disorders. Therefore, both non-invasive and
invasive brain-machine interfaces based on different materials and structures have been widely studied due to their
unique performance. Among these materials, hydrogels have emerged as a promising interface material for brain
signal collection systems due to their similar mechanical properties to biological tissues, excellent biocompatibility,
strong self-adhesive properties, and exceptional ionic conductive characteristics. This review aims to provide an
overview on the recent progress of hydrogel-based brain interfaces in the recording of brain signals with non-
invasive and invasive methods. It is expected that this paper will serve as a valuable summary and reference for
future research in the hydrogel-based brain interface.

Keywords: Brain-machine interface, hydrogels, brain science, flexible electronics, neural signal

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