Optimization of workload level estimation using selection of EEG channel connectivity

Kevin Ardian, Fumihiko Taya, Yu Sun, Anastasios Bezerianos, Tan Kay Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Workload is the amount of cognitive effort executed by a certain subject. Several attempts have been done in order to measure workload level. However, there exists a difficulty in analyzing workload: the problem of individuality, or variability among different individuals and how they respond to similar tasks. In order to have a more objective measure of workload level, the authors employed a more direct analysis upon the system that does cognitive work itself. The use of electroencephalogram (EEG) was employed to measure brain signals and process them to get an objective estimation of workload level. In this study, the authors evaluated the workload level related to complex training-based type task. Piloting simulation task was used to represent such type of task. The authors assess the EEG channel connections and found important connections for the estimation of workload level. This information can be used to build a more an EEG-based workload level estimator that is more efficient, i.e. less channels needed be used to accurately construct the estimation. The authors also found the significant brain signal frequency band that is related to the measure of workload in complex tasks. The problem of individual differences was also resolved using the proposed algorithm.

Original languageEnglish (US)
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1985-1990
Number of pages6
ISBN (Electronic)9781509006229
DOIs
StatePublished - Nov 14 2016
Externally publishedYes
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: Jul 24 2016Jul 29 2016

Other

Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
CountryCanada
CityVancouver
Period7/24/167/29/16

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Keywords

  • Algorithm
  • Brain signal
  • Channel connectivity
  • Electroencephalogram
  • Feature selection
  • Frequency bands
  • Individuality
  • Mental workload
  • Pilot simulation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modeling and Simulation
  • Computer Science Applications
  • Control and Optimization

Cite this

Ardian, K., Taya, F., Sun, Y., Bezerianos, A., & Chen, T. K. (2016). Optimization of workload level estimation using selection of EEG channel connectivity. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 1985-1990). [7744031] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744031