FULLY WIRELESS SLEEP EEG
A portable EEG amplifier streams high-quality EEG data via a wireless connection to a nearby tablet while providing comfort during an all-night recording.
SMARTING SLEEP is specifically designed to provide maximum comfort to the study participants while they dream. A small amplifier is placed on the shoulder belt. Thus, it does not disturb the participants' usual sleeping position. Also, it prevents the formation of artefacts in the physiological signals recorded in real-time.
SMARTING SLEEP is equipped with a 2000mAh Li-polymer battery for continuous wireless streaming of up to 15 hours of EEG data. It is designed according to the American Academy of Sleep Medicine (AASM) standards – the United States professional society for the medical subspecialty of sleep medicine.
SMARTING SLEEP is equipped with additional ExG electrodes in order to capture all the signals necessary for sleep studies. The system consists of 17 EEG channels, 2 mastoid positions, 3 EMG channels and 2 EOG channels. All the recorded data are well synced and recorded in a single file, ready for further analysis.
EEG, EMG, EOG, ECG
0-133Hz flat frequency response High Signal quality
Customizable and Comfortable
for recording head motion
Wireless recordings
All-night recordings
EEG, EMG, EOG, ECG
0-133Hz flat frequency response High Signal quality
Customizable and Comfortable
Wireless recordings
All-night recordings
Phan H. , Chén Y.O. , Koch P. , Lu Z. , McLoughlin I. , Mertins A. , De Vos M. Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning, arXiv:1907.13177.
Sterr A. , Ebajemito J. K. , Mikkelsen K. B. , Bonmati-Carrion M. A. , Santhi N. , della Monica C. , Grainger L. , Atzori G. , Revell V. , Debener S. , Dijk D. , DeVos M. Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study, Frontiers in Human Neuroscience, 2018; 12:452.
Mikkelsen K. B. , Ebajemito J. K. , Bonmati-Carrion M. A. , Santhi N. , Revell V. L. , Atzori G. , Monica C. , Debener S. , Dijk D. , Sterr A. , De Vos M. Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy, J Sleep Res. , 2018; e12786.