Fuzzy rule-based alertness state classification based on the optimization of EEG rhythm/channel combinations

Ahmed Al-Ani, Mostefa Mesbah

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

Abstract

This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We considered four alertness states, namely 'engaged', 'calm', 'drowsy', and 'asleep'. Energies associated with the conventional EEG rhythms, δ, θ, α, ß and γ, extracted from overlapping segments of the different EEG channels were used as features. The proposed method is a two-stage process. In the first stage, the optimal brain regions, represented by a set of EEG channels, are identified. In the second stage, a fuzzy rule-based alertness classification system (FRBACS) is developed to select the optimal EEG rhythms extracted from the previously selected EEG channels. The IF-THEN rules used in FRBACS are constructed using a novel bi-level differential evolution (DE) based search algorithm. Unlike most of the existing classification methods, the proposed classification approach reveals easy to interpret rules that describe each of the alertness states.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2014
PublisherActa Press
Pages146-153
Number of pages8
DOIs
Publication statusPublished - 2014
EventIASTED International Conference on Biomedical Engineering, BioMed 2014 - Zurich, Switzerland
Duration: Jun 23 2014Jun 25 2014

Other

OtherIASTED International Conference on Biomedical Engineering, BioMed 2014
CountrySwitzerland
CityZurich
Period6/23/146/25/14

Keywords

  • Alertness classification
  • Differential Evolution
  • Drowsiness
  • EEG
  • Fuzzy Rule-Based Classification System
  • Variable selection

ASJC Scopus subject areas

  • Modelling and Simulation

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  • Cite this

    Al-Ani, A., & Mesbah, M. (2014). Fuzzy rule-based alertness state classification based on the optimization of EEG rhythm/channel combinations. In Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2014 (pp. 146-153). Acta Press. https://doi.org/10.2316/P.2014.818-022