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Abstract. Microsleeps (MS) are a frequently discussed topic due to their fatal consequences. Their detection is necessary for the purpose of sleep laboratories, where they provide an option for the quantifying rate of sleep deprivation level and objective evaluation of subjective sleepiness. Many studies are dealing with this topic for automotive usage to design a fatigue countermeasure device. We made a research of recent attitude to the development of the automated MS detection methods.We created an overview of several MS detection approaches based on the measurement of biological signals. We also summarized the changes in EEG, EOG and ECG signals, which have been published over the last few years.The reproducible changes in the entire EEG spectrum, primarily with the increased activity of delta and theta, were noticed during a transition to fatigue. There were observed changes of blinking rate and reduction of eye movements during the fatigue tasks. MS correspond with variations in the autonomic regulation of the cardiovascular function, which can be quantified by HRV parameters. The decrease in HR, VLF, and LF/HF before falling asleep was revealed.EEG signal, especially its slow wave activity, considered to be the most predictive and reliable for the level of alertness. In spite of the detection from EEG signal is the most common method, EOG based approaches can also be very efficient and more driver-friendly. Besides, the signal processing in the time domain can improve the detection accuracy of the short events like MS.

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