  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 micro-sleep
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.
