Selection of input features across subjects for classifying crewmember workload using artificial neural networks

Authors:
T. I. Laine;K. W. Bauer;J. W. Lanning;C. A. Russell;G. F. Wilson
Affiliations:
Dept. of Operational Sci., Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA;-;-;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2002

Citing 0
Cited 7

Measuring saliency of features extracted by model-based methods from internal carotid arterial Doppler signals using signal-to-noise ratios

Digital Signal Processing
Measuring Saliency of Features Using Signal-to-noise Ratios for Detection of Electrocardiographic Changes in Partial Epileptic Patients

Journal of Medical Systems
Fundamentals of physiological computing

Interacting with Computers
Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for Doppler ultrasound signals

Engineering Applications of Artificial Intelligence
Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Expert Systems with Applications: An International Journal
On the recording reference contribution to EEG correlation, phase synchorony, and coherence

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards an adaptive cultural heritage experience using physiological computing

CHI '13 Extended Abstracts on Human Factors in Computing Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The issue of crewmember workload is important in complex system operation because operator overload leads to decreased mission effectiveness. Psychophysiological research on mental workload uses measures such as electroencephalogram (EEG), cardiac, eye-blink, and respiration measures to identify mental workload levels. This paper reports a research effort whose primary objective was to determine if one parsimonious set of salient psychophysiological features can be identified to accurately classify mental workload levels across multiple test subjects performing a multiple task battery. To accomplish this objective, a stepwise multivariate discriminant analysis heuristic and artificial neural network feature selection with a signal-to-noise ratio (SNR) are used. In general, EEG power in the 31-40-Hz frequency range and ocular input features appeared highly salient. The second objective was to assess the feasibility of a single model to classify mental workload across different subjects. A classification accuracy of 87% was obtained for seven independent validation subjects using neural network models trained with data from other subjects. This result provides initial evidence for the potential use of generalized classification models in multitask workload assessment.