Physiological Indices of Mental Workload
Abstract:
We are working on an enabling technology to facilitate the development of physiological indices of mental workload that could be used in high-performance aircraft. To date, we have designed and implemented the core components of a neural-network based algorithm for deriving continuous mental workload indices from continuous recordings of brain, scalp muscle, eye and heart electrical activity. We also have designed an experiment to test the adequacy of this algorithm, and have developed technologies to perform the experiment, including the following 1 designing a task battery to initially test the ability of the network algorithm to generalize across cognitive functions relevant to piloting aircraft and 2 implementing a software library that could be used to efficiently present the task stimuli using the same personal computer which also collects 32 channels of electrophysiological data. We have tested the integrated system and have found it capable of providing accurate timing of task stimuli, subject responses, and electrophysiological data.