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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 14, 1992
Accession Number
ADA261692

Entities

People

  • Alan S. Gevins
  • Harrison M. Leong

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Biological Sciences
  • Classification
  • Cognitive Workload
  • Computer Programming
  • Computer Programs
  • Computers
  • Control Systems
  • Detection
  • Human Factors Engineering
  • Neural Networks
  • New York
  • Pattern Recognition
  • Signal Processing
  • Workload

Fields of Study

  • Computer science

Readers

  • Aviation Science / Aeronautics.
  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML