AUTHOR=Sterr Annette , Ebajemito James K. , Mikkelsen Kaare B. , Bonmati-Carrion Maria A. , Santhi Nayantara , della Monica Ciro , Grainger Lucinda , Atzori Giuseppe , Revell Victoria , Debener Stefan , Dijk Derk-Jan , DeVos Maarten 

TITLE=Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 12 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2018.00452

DOI=10.3389/fnhum.2018.00452

ISSN=1662-5161

ABSTRACT=EEG recordings represent a vital component of the assessment of sleep physiology, but the methodology presently used is costly, intrusive to participants and laborious in application. There is a recognized need to develop more easily applicable yet reliable EEG systems that allow unobtrusive long-term recording of sleep-wake EEG, ideally away from the laboratory setting. cEEGrid is a recently developed flex-printed around-the-ear electrode array which holds great potential for sleep-wake monitoring research. It is comfortable to wear, simple to apply and minimally intrusive during sleep. Moreover, it can be combined with a smartphone-controlled miniaturized amplifier and is fully portable. Evaluation of cEEGrid as a motion-tolerant device is ongoing, but initial findings clearly indicate that it is very well suited for cognitive research. The present study aimed to explore the suitability of cEEGrid for sleep research, by testing whether cEEGrid data affords the signal quality and characteristics necessary for sleep stage scoring. cEEGrid sleep data was obtained simultaneously with a standard PSG system in an accredited sleep laboratory. Twenty participants were recorded for one extended nocturnal sleep opportunity. The data from both systems were manually scored to obtain the hypnograms respectively. Sleep parameters relating to sleep maintenance and sleep architecture were then extracted and statistically assessed for signal quality and concordance. The findings identify the cEEGrid system as a viable and robust recording tool that captures the key characteristics of sleep and wake EEG well enough to allow for manual sleep staging.