Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model

A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: (1) lack of generalizability, (2) failure to address individual variability in generalized models, and/or (3) lack of a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief “identification” tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability.

► In this study a drowsiness–alertness continuum algorithm was developed.
► The algorithm was validated across multiple individuals (n = 160) and multiple cognitive and driving tasks.
► The drowsiness metric was able to track errors when participants were sleep deprived (but not when rested).
► Further investigation is required to determine field applicability.