Paper Info
Title
Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG
Abstract
A micro-sleep is a short sleep that lasts from 1 to 30 secs. Its detection during driving is crucial to prevent accidents that could claim a lot of people's lives. Electroencephalogram (EEG) is suitable to detect micro-sleep because EEG was associated with consciousness and sleep. Deep learning showed great performance in recognizing brain states, but sufficient data should be needed. However, collecting micro-sleep data during driving is inefficient and has a high risk of obtaining poor data quality due to noisy driving situations. Night-sleep data at home is easier to collect than micro-sleep data during driving. Therefore, we proposed a deep learning approach using night-sleep EEG to improve the performance of micro-sleep detection. We pre-trained the U-Net to classify the 5-class sleep stages using night-sleep EEG and used the sleep stages estimated by the U-Net to detect micro-sleep during driving. This improved micro-sleep detection performance by about 30% compared to the traditional approach. Our approach was based on the hypothesis that microsleep corresponds to the early stage of non-rapid eye movement (NREM) sleep. We analyzed EEG distribution during night-sleep and micro-sleep and found that micro-sleep has a similar distribution to NREM sleep. Our results provide the possibility of similarity between micro-sleep and the early stage of NREM sleep and help prevent micro-sleep during driving.
Year
DOI
Venue
2021
10.1109/BCI51272.2021.9385325
2021 9th International Winter Conference on Brain-Computer Interface (BCI)
Keywords
DocType
ISSN
Deep learning,Sleep,Data integrity,Brain modeling,Electroencephalography,Brain-computer interfaces,Noise measurement
Conference
2572-7680
ISBN
Citations 
PageRank 
978-1-7281-8485-2
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Young-Seok Kweon101.35
Heon-Gyu Kwak200.34
Gi-Hwan Shin301.35
Minji Lee401.35