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 proceeding 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, Part 1
EditorsMinho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
Place of PublicationHeidelberg
PublisherSpringer, Springer Nature
Pages176-183
Number of pages8
Volume8226
ISBN (Electronic)9783642420542
ISBN (Print)9783642420535
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 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)03029743
ISSN (Electronic)16113349

Other

Other20th International Conference on Neural Information Processing, ICONIP 2013
CountryKorea, Republic of
CityDaegu
Period3/11/137/11/13

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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 M. Lee, A. Hirose, Z-G. Hou, & R. M. Kil (Eds.), Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings, Part 1 (Vol. 8226, 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). Heidelberg: Springer, Springer Nature. https://doi.org/10.1007/978-3-642-42054-2_23