Document Type

Article

Publication Date

5-1-2021

City

Corvallis

State

OR

Abstract

Augmented cognition is a form of human-systems interaction in which physiological sensing of a user’s cognitive state is used to precisely invoke system automations when needed. The present study monitored the in-flight physiological state of the pilot to determine the optimal combination of EEG indices to predict variations in workload, or opportunities for augmented cognition.The [sic] participants were 10 collegiate aviation students with FAA commercial pilot certificates and current medical certificates. Each participant performed a uniform flight scenario that included procedures that varied in workload demands. All maneuvers were performed while simultaneously acquiring EEG data in flight. The EEG data were divided into periods of high and low workload. Power spectral density values were computed and subjected to several machine learning methods to distinguish high and low workload periods. The results indicate excellent classification accuracy for distinguishing low and high workload. The present results further demonstrate the potential of augmented cognition.


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