Operator State Classification Developer's Toolkit

Operators of complex systems can become inefficient because of mental overload, fatigue, boredom and lapses in situational awarness, drug effects, illness, and other factors. A reliable means of detecting and monitoring operator mental states would allow an intelligent system to alert the operator in the case of mental lapses, or it could adaptively automate a subset of control functions in the case of operator overload. Physiological and neurophysiological signals such as eye movements, the electroencephalogram, heart rate, and respiration can be used to classify operator mental states. Indeed, laboratory research has demonstrated that such measures can be used to identify which of several cognitive tasks an operator is engaged in, the level of cognitive load an operator is being subjected to, and whether he or she is fatigued or otherwise impared. Improved technology for research by professional psychophysiologists is needed to develop such measures to the point where they could be used in operational settings. We thus propose to develop an "Operator State Classification Developer's Toolkit" by extending the functionality of our commercial MANSCAN research brain scanner for this application. The Phase I effort will focus on system design issues.