Performance Improvement of Driving Fatigue Identification Based on Power Spectra and Connectivity Using Feature Level and Decision Level Fusions

Jonathan Harvy, Evangelos Sigalas, Nitish Thakor, Anastasios Bezerianos, Junhua Li

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

Abstract

Power and connectivity features extracted from EEG signals have been previously utilized to detect mental fatigue during driving. Although each of the feature categories has the discriminative power to differentiate alert and fatigue states, they might represent different aspects of information relevant to fatigue identification. Two fusion methods, feature level and decision level fusions, were proposed in this study to combine individual channel information (i.e., power features) and between-channel information (i.e., connectivity features). According to the results of the study, the average accuracies of the fusion methods were higher than the accuracies of the individual feature categories (feature level fusion: 84.70%, decision level fusion: 87.13%, power features: 80.82%, connectivity features: 79.36%). The statistical analysis demonstrated that the two fusion methods significantly improved the classification performance of driving fatigue. The fusion methods proposed in this study can be embedded into a driving fatigue detection system for a practical use in a vehicle.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages102-105
Number of pages4
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Externally publishedYes
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Harvy, J., Sigalas, E., Thakor, N., Bezerianos, A., & Li, J. (2018). Performance Improvement of Driving Fatigue Identification Based on Power Spectra and Connectivity Using Feature Level and Decision Level Fusions. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 102-105). [8512259] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512259