A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors

Lun De Liao, Shang Lin Wu, Chang Hong Liou, Shao Wei Lu, Shi An Chen, Sheng Fu Chen, Li-Wei Ko, Chin Teng Lin

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer's disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a sizeadjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions.

Original languageEnglish
Article number6704315
Pages (from-to)1545-1555
Number of pages11
JournalIEEE Transactions on Instrumentation and Measurement
Volume63
Issue number6
DOIs
StatePublished - 1 Jan 2014

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