Vigilance differentiation from EEG complexity attributes

Junhua Li, Indu Prasad, Justin Dauwels, Nitish V Thakor, Hasan Ai-Nashash

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

Abstract

Vigilance is an ability to maintain concentrated attention on a particular event or target stimulus. Monitoring tasks require certainly high vigilance to properly detect rare occurrence or accurately respond to stimulation. Changes in vigilance can be reflected by EEG signal, so vigilance levels can be classified based on features extracted from EEG. Up to now, power spectral density was commonly employed as features to differentiate between vigilance levels in majority of previous studies. To the best of our knowledge, multifractal attributes for vigilance differentiation have not been exploited, and their feasibility still need to be investigated. In this study, we first extracted multifractal attributes based on wavelet leaders, and then selected statistically significant distinct attributes for the following classification (two vigilance levels). According to the results, classification accuracy was improved with increase of time window used for feature extraction. When time window was increased to 50 s, an averaged accuracy of 91.67% was achieved, and accuracies for all subjects were higher than 85 %. Our results suggest that multifractal attributes are promising for vigilance differentiation.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages199-206
Number of pages8
Volume9492
ISBN (Print)9783319265605
DOIs
StatePublished - 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: Nov 9 2015Nov 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9492
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other22nd International Conference on Neural Information Processing, ICONIP 2015
CountryTurkey
CityIstanbul
Period11/9/1511/12/15

Fingerprint

Electroencephalography
Attribute
Power spectral density
Time Windows
Feature extraction
Power Spectral Density
Monitoring
Differentiate
Feature Extraction
Wavelets
Distinct
Target
Electroencephalogram

Keywords

  • Complexity attribute
  • Feature selection
  • Self-similarity
  • Sustained attention
  • Vigilance classification

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Li, J., Prasad, I., Dauwels, J., Thakor, N. V., & Ai-Nashash, H. (2015). Vigilance differentiation from EEG complexity attributes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9492, pp. 199-206). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492). Springer Verlag. https://doi.org/10.1007/978-3-319-26561-2_24

Vigilance differentiation from EEG complexity attributes. / Li, Junhua; Prasad, Indu; Dauwels, Justin; Thakor, Nitish V; Ai-Nashash, Hasan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9492 Springer Verlag, 2015. p. 199-206 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492).

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

Li, J, Prasad, I, Dauwels, J, Thakor, NV & Ai-Nashash, H 2015, Vigilance differentiation from EEG complexity attributes. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9492, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9492, Springer Verlag, pp. 199-206, 22nd International Conference on Neural Information Processing, ICONIP 2015, Istanbul, Turkey, 11/9/15. https://doi.org/10.1007/978-3-319-26561-2_24
Li J, Prasad I, Dauwels J, Thakor NV, Ai-Nashash H. Vigilance differentiation from EEG complexity attributes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9492. Springer Verlag. 2015. p. 199-206. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26561-2_24
Li, Junhua ; Prasad, Indu ; Dauwels, Justin ; Thakor, Nitish V ; Ai-Nashash, Hasan. / Vigilance differentiation from EEG complexity attributes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9492 Springer Verlag, 2015. pp. 199-206 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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