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Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. Many methods for vigilance detection, based on biological and behavioural characteristics, have been proposed in the literature. In general, the existing approaches do not provide any solution to measure vigilance level quantitatively and adopt costly equipment. This paper utilizes a portable EEG device and presents a new method for estimation of vigilance level of an individual by utilizing ERPs (P300 and N100) of EEG signals and eye blink rate. Here, we propose a fuzzy rule-based system using amplitude and time variations of ERPs (N100 and P300) and blink variability to establish the correlation among N100, P300, eye blink and the vigilance activity. Besides, with the help of our proposed fuzzy model we efficiently calculate and quantify the vigilance level, and thereby obtain a numerical value of vigilance instead of its mere presence or absence. To validate the results obtained from our fuzzy model, we performed subjective analysis (to assess the mood and stress level of participants), reaction time analysis and compared the vigilance values with target detection accuracy. The obtained results prove the efficacy of our proposal.

Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. It is an important aspect in industries such as aerospace and nuclear power, which involve tremendous man-machine interaction and where safety of any component/system or environment as a whole is extremely crucial. Many methods for vigilance detection, based on biological and behavioral characteristics, have been proposed in the literature. Nevertheless, the existing methods are associated with high time complexity, unhandy devices and incur huge equipment overhead. This paper aims to pave an alternative solution to the existing techniques using brain computing interface (BCI). EEG device being a non-invasive BCI technique is popular in many applications. In this work, we have utilized P300 component of ERPs of EEG signal for vigilance detection task as it can be detected fast and accurately. Through this work, we aim to establish the correlation between P300 ERP and vigilance. We have performed a number of experiments to substantiate the correctness of our proposal and have also proposed an approach to measure the vigilance level.