Spectrum Estimation of Odor EEG Responses with Parametric-Nonparametric Spectral Analysis Methods

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dc.contributor.authorSeker, Mesut
dc.contributor.authorAkin, Mehmet
dc.contributor.authorOzerdem, Mehmet Sirac
dc.date.accessioned2024-04-24T17:38:07Z
dc.date.available2024-04-24T17:38:07Z
dc.date.issued2018
dc.departmentDicle Üniversitesien_US
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.description.abstractIt is known that external stimulus such as visual, auditory and odor have effect on brain activity. Effects of odor stimuli, which has complex structure, on central nerveous system is lack of knowledge in literature. The goal of proposed study is to show how pleasant-unpleasant odors effect brain waves by using spectral analysis methods and discriminating different odors through statistical methods and a classifier. The EEG dataset used in study was taken from 6 participants while their eyes are closed and 4 odor (2 pleasant-2unpleasant) stimulus were applied to them using 14 chanelled EMOTIV-EPOC headset. Discrete Wavelet Transform (DWT) was used to pre-processed signals obtained from embedded filters to extract more meaningful EEG sub-bands (delta-tetha-alpha-beta). First of all, power spectrum graphics of these sub-bands was drawn using Welch's method to see how pleasant-unpleasant odor EEGs behave. Then, spectrum coefficients were gained by help of parametric (Burg, Yule-Walker, Covariance, Modified Covariance) and non-parametric (Welch's) methods. Selected feature vectors from these coefficients were classified. Selected features are min, max value and standard deviation. k-NN was chosen for classification algorithm. Avarage power spectrum analysis showed that unpleasant odor EEG has higher values than pleasant one with respect to all sub-bands. Parametric methods gave better results to discriminate odor EEGs. Burg method has highest classification rate among others.en_US
dc.description.sponsorshipIEEE,Huawei,Aselsan,NETAS,IEEE Turkey Sect,IEEE Signal Proc Soc,IEEE Commun Soc,ViSRATEK,Adresgezgini,Rohde & Schwarz,Integrated Syst & Syst Design,Atilim Univ,Havelsan,Izmir Katip Celebi Univen_US
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85050792450
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11468/21333
dc.identifier.wosWOS:000511448500082
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 26th Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmotiv Epocen_US
dc.subjectOdoren_US
dc.subjectEegen_US
dc.subjectWelchen_US
dc.subjectBurgen_US
dc.subjectYule Walkeren_US
dc.subjectCovarianceen_US
dc.subjectModified Covarianceen_US
dc.subjectDwten_US
dc.titleSpectrum Estimation of Odor EEG Responses with Parametric-Nonparametric Spectral Analysis Methodsen_US
dc.titleSpectrum Estimation of Odor EEG Responses with Parametric-Nonparametric Spectral Analysis Methods
dc.typeConference Objecten_US

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