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

Published on May 1, 2011in Biological Psychology2.63
· DOI :10.1016/j.biopsycho.2011.03.003
Robin Johnson12
Estimated H-index: 12
(UCLA: University of California, Los Angeles),
Djordje Popovic11
Estimated H-index: 11
(UCLA: University of California, Los Angeles)
+ 3 AuthorsChris Berka15
Estimated H-index: 15
(UCLA: University of California, Los Angeles)
Abstract 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.
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