EEG-Based Classification of Olfactory Response to Pleasant Stimuli

Nida Itrat Abbasi, Rohit Bose, Anastasios Bezerianos, Nitish V. Thakor, Andrei Dragomir

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

Abstract

Olfactory perception involves complex processing distributed along several cortical and sub-cortical regions in the brain. Although several studies have shown that the power spectra of the electroencephalography (EEG) contain information that can be used to differentiate between pleasant and unpleasant stimuli, there are still no studies which investigate whether EEG can be used to differentiate between the neural responses to olfactory stimuli of different levels of pleasantness. For this purpose, in the present study, local brain information within established frequency bands (θ, α and γ) has been used to devise discriminative features in a classification approach. A comparative study of four widely used classifiers is presented and SVM gives the best performance (accuracy = 75.71%). The results reveal that is it possible to objectively discriminate using EEG spectral features between fine levels of perceived pleasantness using the SVM-based classifier within a cross-validation procedure.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5160-5163
Number of pages4
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

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

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/TerritoryGermany
CityBerlin
Period7/23/197/27/19

ASJC Scopus subject areas

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

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