Fuzzy logic-based automatic alertness state classification using multi-channel EEG data

Ahmed Al-Ani, Mostefa Mesbah, Bram Van Dun, Harvey Dillon

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

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages176-183
Number of pages8
Volume8226 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: Nov 3 2013Nov 7 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Neural Information Processing, ICONIP 2013
CountryKorea, Republic of
CityDaegu
Period11/3/1311/7/13

Keywords

  • Alertness classification
  • EEG
  • Fuzzy rule-based system

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Al-Ani, A., Mesbah, M., Van Dun, B., & Dillon, H. (2013). Fuzzy logic-based automatic alertness state classification using multi-channel EEG data. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings (PART 1 ed., Vol. 8226 LNCS, pp. 176-183). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8226 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-42054-2_23