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Fuzzy Logic-Based Automatic Alertness State Classification Using Multi-channel EEG Data

  • Ahmed Al-Ani
  • Mostefa Mesbah
  • Bram Van Dun
  • Harvey Dillon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

This paper represents an attempt to automatically classify alertness state using information extracted from multi-channel EEG. To reduce the amount of data and improve the performance, a channel selection method based on support vector machine (SVM) classifier has been performed. The features used for the EEG channel selection process and subsequently for alertness classification represent the energy values of the five EEG rhythms; namely δ, θ, α, β and γ. In order to identify the feature/channel combination that leads to the best alertness state classification performance, we used a fuzzy rule-based classification system (FRBCS) that utilizes differential evolution in constructing the rules. The results obtained using the FRBCS were found to be comparable to those of SVM but with the added advantage of revealing the rhythm/channel combination associated with each alertness state.

Keywords

Alertness classification EEG fuzzy rule-based system 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ahmed Al-Ani
    • 1
  • Mostefa Mesbah
    • 2
  • Bram Van Dun
    • 3
  • Harvey Dillon
    • 3
  1. 1.Faculty of Engineering and Information TechnologyUniversity of Technology, SydneyUltimoAustralia
  2. 2.School of Computer Science and Software EngineeringThe University of Western AustraliaCrawleyAustralia
  3. 3.National Acoustic Laboratories, Australian Hearing Hub, Level 4Macquarie UniversityAustralia

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