脑电图
计算机科学
信号(编程语言)
材料科学
生物医学工程
神经科学
工程类
生物
程序设计语言
作者
Jiabei Luo,Chuanyue Sun,Boya Chang,Jing Wang,Kerui Li,Yaogang Li,Qinghong Zhang,Hongzhi Wang,Chengyi Hou
出处
期刊:ACS Nano
[American Chemical Society]
日期:2022-10-24
卷期号:16 (11): 19373-19384
被引量:35
标识
DOI:10.1021/acsnano.2c08961
摘要
Human-machine interaction plays a significant role in promoting convenience, production efficiency, and usage experience. Because of the universality and characteristics of electroencephalogram (EEG) signals, active EEG interaction is a promising and cutting-edge method for human-machine interaction. The seamless, skin-compliant, and motion-robust human-machine interface (HMI) for active EEG interaction has been in focus. Herein, we report a self-adaptive HMI (PAAS-MXene hydrogel) that can activate rapid gelation (5 s) using MXene cross-linking and conformably self-adapt to the scalp to help improve signal transduction. In addition to exhibiting satisfactory skin compliance, appropriate adhesion, and good biocompatibility, PAAS-MXene has demonstrated electrical performance reliability, such as low impedance (<50 Ω) at physiologically relevant frequencies, stable polarization potential (the rate of change is less than 6.5 × 10-4 V/min), negligible ion conductivity, and impedance change after 1000 stretch cycles, thereby realizing acquisition of EEG signals. In addition, a cap-free EEG signal acquisition method based on PAAS-MXene has been proposed. These findings confirm the high-precision detection ability of PAAS-MXene for electrocardiogram signals and EEG signals. Therefore, PAAS-MXene offers an option to actively control intention, motion, and vision through active EEG signals.
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