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


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 publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319265605
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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other22nd International Conference on Neural Information Processing, ICONIP 2015


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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