A Neurophysiological Sensor Suite for Real-Time Prediction of Pilot Workload in Operational Settings

Abstract

In recent years, research involving the use of neurophysiological sensor streams to quantitatively measure and predict the level of mental workload experienced by an individual user has gained momentum as the complexity of the tasks operators have experienced in heavily computerized contexts has continued to expand. Despite the promising results from many empirical studies reporting successful classification of workload using neurophysiological sensor data, accurate classification of workload in real-time remains a largely unsolved problem. This research aims to both introduce and examine the efficacy of a new research tool: Tools for Object Measurement and Evaluation (TOME). The TOME system is a toolset for collating and examining neurophysiological data in real time. Following a presentation of the system, and the problems the system may help to solve, a validation study using the TOME system is presented.