脑电图
信号(编程语言)
石墨烯
计算机科学
材料科学
大脑活动与冥想
生物医学工程
人工智能
纳米技术
神经科学
工程类
心理学
程序设计语言
作者
Shaikh Nayeem Faisal,Tien-Thong Nguyen,Tasauf Torzo,Daniel J. Leong,Aiswarya Pradeepkumar,Chin‐Teng Lin,Francesca Iacopi
出处
期刊:ACS applied nano materials
[American Chemical Society]
日期:2023-03-16
卷期号:6 (7): 5440-5447
被引量:6
标识
DOI:10.1021/acsanm.2c05546
摘要
The availability of accurate and reliable dry sensors for electroencephalography (EEG) is vital to enable large-scale deployment of brain–machine interfaces (BMIs). However, dry sensors invariably show poorer performance compared to the gold standard Ag/AgCl wet sensors. The loss of performance with dry sensors is even more evident when monitoring the signal from hairy and curved areas of the scalp, requiring the use of bulky and uncomfortable acicular sensors. This work demonstrates three-dimensional micropatterned sensors based on a subnanometer-thick epitaxial graphene for detecting the EEG signal from the challenging occipital region of the scalp. The occipital region, corresponding to the visual cortex of the brain, is key to the implementation of BMIs based on the common steady-state visually evoked potential paradigm. The patterned epitaxial graphene sensors show efficient on-skin contact with low impedance and can achieve comparable signal-to-noise ratios against wet sensors. Using these sensors, we have also demonstrated hands-free communication with a quadruped robot through brain activity.
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