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Open AccessArticle

Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems

1
Centre for AI, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney NSW 2007, Australia
2
Brain Research Center, National Chiao Tung University, Hsinchu 300, Taiwan
3
Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
4
Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli 35053, Taiwan
*
Authors to whom correspondence should be addressed.
Micromachines 2019, 10(11), 720; https://doi.org/10.3390/mi10110720
Received: 5 September 2019 / Revised: 21 October 2019 / Accepted: 24 October 2019 / Published: 25 October 2019
(This article belongs to the Special Issue Recent Advances in Devices for Human Brain Imaging)
A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system. View Full-Text
Keywords: electroencephalography (EEG); brain–computer interface (BCI); dry sensor; event-related potential (ERP) electroencephalography (EEG); brain–computer interface (BCI); dry sensor; event-related potential (ERP)
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Lin, C.-T.; Liu, C.-H.; Wang, P.-S.; King, J.-T.; Liao, L.-D. Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems. Micromachines 2019, 10, 720.

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