Selecting Salient Features of Psychophysiological Measures

Abstract

Determining operator cognitive or functional state is a critical component of adaptive aiding systems. To determine cognitive state, we must decide which measured features from the human will assist in distinguishing different levels of mental activity. A battery of psychophysiological signals was collected for two levels of cognitive workload from which 43 measures were derived. Three feature-reduction methods, principal component analysis, a weight-based partial derivative method, and a weight-based signal-to-noise ratio were applied, and the results were used as inputs to an artificial neural network for training and classification. Average classification accuracies up to 89.7 percent were achieved and the number of input features required was reduced by up to 84 percent.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2001
Accession Number
ADA396165

Entities

People

  • Chris A. Russel
  • Steve G. Gustafson

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Adaptive Control Systems
  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Algorithms
  • Classification
  • Cognitive Workload
  • Data Science
  • Data Sets
  • Engineering
  • Factor Analysis
  • Feature Extraction
  • Information Science
  • Machine Learning
  • Neural Networks
  • Workload

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks