A Stretchable, Sensitive, and Stable Cluster‐Hydrogel Electrode for Long‐Term Clinical Surveillance of Parkinson's Disease

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Authors: Yuqin Zhang, Yufeng Ke, Shuangjie Liu, Yang Wang, Leyi Jia, Xinyu Sun, Shaoya Yin, Zhuo Chen, Zhiyuan Chen, Zhaoliang Ni, Wanyu Tang, Xiaoyi Zuo
+9 more Xinqin Xiao, Ziying Meng, Hongzuo Chu, Mai Xu, Jiang Yang, Xiaoguang Tong, Hao Wang, Dong Ming, Xiao‐Dong Zhang
Cited by: 4
References: 39

Abstract

ABSTRACT Neural recording electrodes are pivotal components of a brain‐computer interface (BCI) for diagnosing neurological diseases. However, the conventional electroencephalography (EEG) electrodes face challenges in long‐term stability and biocompatibility. The contemporary dry electrodes offer enhanced stability but remain suboptimal in recording sensitivity. To address these constraints, we developed mechanically flexible, high‐sensitivity, biocompatible electrodes employing a redox‐active multienzyme‐mimetic gold cluster integrated into highly conductive polymer hydrogel networks for prolonged neural monitoring in Parkinson's disease (PD). The resulting cluster‐hydrogel (CH) electrode exhibits an ultralow contact impedance of 6.9 kΩ·cm 2 , equivalent to 1/6 th that of conventional Ag/AgCl electrodes and 1/24 th that of metallic electrodes, while sustaining a high signal‐to‐noise ratio of 6.6 dB over 12 h continuous recording. Clinically, through the integration of machine learning techniques, the CH electrodes enabled accurate PD diagnosis via alpha reactivity and entropy analyses, achieving an area under the curve (AUC) of 0.9 (KNN classifier) and a phase‐locking value (PLV) Pearson correlation of 0.934. In addition, the CH electrodes outperformed conventional EEG electrodes in alpha‐band sensitivity, achieving a 16.2‐fold higher signal‐to‐noise ratio that enhanced the detection of clinically diagnostic electrophysiological signatures in PD. Combined with high biocompatibility, the CH electrodes hold translational promise as an efficient, safe, and stable neural recording system for clinical BCIs and neuroscience applications.

Fields of Study & Subjects

Neuroscience and Neural Engineering Advanced Sensor and Energy Harvesting Materials Neurological disorders and treatments

Publication details

Journal / source
Advanced Functional Materials
Publisher
Wiley
Volume
36
Issue
42
Publication date
15 March 2026
Language
EN