Like/Dislike Analysis Using EEG: Determination of Most Discriminative Channels and Frequencies

dc.contributor.author Yilmaz, Bulent
dc.contributor.author Korkmaz, Sumeyye
dc.contributor.author Arslan, Dilek Betul
dc.contributor.author Gungor, Evrim
dc.contributor.author Asyali, Musa H.
dc.date.accessioned 2025-09-25T10:50:02Z
dc.date.available 2025-09-25T10:50:02Z
dc.date.issued 2014
dc.description Yilmaz, Bulent/0000-0003-2954-1217; Arslan-Saridede, Dilek Betul/0000-0002-1124-3695; en_US
dc.description.abstract In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of "like" decisions during such mental processes. For this purpose, we have obtained multi-channel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case. No button press meant that the subject did not like the particular shoe that was displayed and corresponding epoch designated as a DISLIKE case. After preprocessing, power spectral density (PSD) of EEG data was estimated at different frequencies (4, 5, ... , 40 Hz) using the Burg method, for each epoch corresponding to one shoe presentation. Each subject's data consisted of normalized PSD values (NPVs) from all LIKE and DISLIKE cases/epochs coming from all 19 EEG channels. In order to determine the most discriminative frequencies and channels, we have utilized logistic regression, where LIKE/DISLIKE status was used as a categorical (binary) response variable and corresponding NPVs were the continuously valued input variables or predictors. We observed that when all the NPVs (total of 37) are used as predictors, the regression problem was becoming ill-posed due to large number of predictors (compared to the number of samples) and high correlation among predictors. To circumvent this issue, we have divided the frequency band into low frequency (LF) 4-19 Hz and high frequency (HF) 20-40 Hz bands and analyzed the influence of the NPV in these bands separately. Then, using the p-values that indicate how significantly estimated predictor weights are different than zero, we have determined the NPVs and channels that are more influential in determining the outcome, i. e., like/dislike decision. In the LF band, 4 and 5 Hz were found to be the most discriminative frequencies (MDFs). In the HF band, none of the frequencies seemed offer significant information. When both male and female data was used, in the LF band, a frontal channel on the left (F7-A1) and a temporal channel on the right (T6-A2) were found to be the most discriminative channels (MDCs). In the HF band, MDCs were central (Cz-A1) and occipital on the left (O1-A1) channels. The results of like timings suggest that male and female behavior for this set of stimulant images were similar. (C) 2013 Elsevier Ireland Ltd. All rights reserved. en_US
dc.identifier.doi 10.1016/j.cmpb.2013.11.010
dc.identifier.issn 0169-2607
dc.identifier.issn 1872-7565
dc.identifier.scopus 2-s2.0-84892816491
dc.identifier.uri https://doi.org/10.1016/j.cmpb.2013.11.010
dc.identifier.uri https://hdl.handle.net/20.500.12573/4124
dc.language.iso en en_US
dc.publisher Elsevier Ireland Ltd en_US
dc.relation.ispartof Computer Methods and Programs in Biomedicine en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Neuromarketing en_US
dc.subject EEG en_US
dc.subject Partiality en_US
dc.subject Power Spectral Density en_US
dc.subject Burg Method en_US
dc.subject Logistic en_US
dc.subject Regression en_US
dc.title Like/Dislike Analysis Using EEG: Determination of Most Discriminative Channels and Frequencies en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.id Yilmaz, Bulent/0000-0003-2954-1217
gdc.author.id Arslan-Saridede, Dilek Betul/0000-0002-1124-3695
gdc.author.scopusid 57189925966
gdc.author.scopusid 57213657656
gdc.author.scopusid 55948705300
gdc.author.scopusid 55949130900
gdc.author.scopusid 55948103700
gdc.author.wosid Yilmaz, Bulent/Juz-1320-2023
gdc.author.wosid Arslan-Saridede, Dilek Betul/Aas-4281-2020
gdc.author.wosid Arslan-Saridede, Dilek/Aas-4281-2020
gdc.bip.impulseclass C4
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access metadata only access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department Abdullah Gül University en_US
gdc.description.departmenttemp [Yilmaz, Bulent] Abdullah Gul Univ, Dept Elect Engn & Elect, Kayseri, Turkey; [Korkmaz, Sumeyye; Arslan, Dilek Betul; Gungor, Evrim] Erciyes Univ, Biomed Engn Dept, Kayseri, Turkey; [Asyali, Musa H.] Antalya Int Univ, Dept Elect Engn & Elect, Antalya, Turkey en_US
gdc.description.endpage 713 en_US
gdc.description.issue 2 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 705 en_US
gdc.description.volume 113 en_US
gdc.description.woscitationindex Science Citation Index Expanded
gdc.description.wosquality Q1
gdc.identifier.openalex W2047506651
gdc.identifier.pmid 24326336
gdc.identifier.wos WOS:000330137600026
gdc.index.type WoS
gdc.index.type Scopus
gdc.index.type PubMed
gdc.oaire.diamondjournal false
gdc.oaire.impulse 9.0
gdc.oaire.influence 5.4155973E-9
gdc.oaire.isgreen false
gdc.oaire.keywords Adult
gdc.oaire.keywords Male
gdc.oaire.keywords Young Adult
gdc.oaire.keywords Decision Making
gdc.oaire.keywords Humans
gdc.oaire.keywords Electroencephalography
gdc.oaire.keywords Female
gdc.oaire.popularity 3.2320177E-8
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0302 clinical medicine
gdc.oaire.sciencefields 05 social sciences
gdc.oaire.sciencefields 0501 psychology and cognitive sciences
gdc.openalex.collaboration National
gdc.openalex.fwci 4.7385
gdc.openalex.normalizedpercentile 0.94
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 66
gdc.plumx.crossrefcites 13
gdc.plumx.mendeley 204
gdc.plumx.patentfamcites 1
gdc.plumx.pubmedcites 15
gdc.plumx.scopuscites 69
gdc.scopus.citedcount 74
gdc.virtual.author Erkantarcı, Betül
gdc.wos.citedcount 57
relation.isAuthorOfPublication 81098d59-1894-45fd-92e5-9903b66fc2a8
relation.isAuthorOfPublication.latestForDiscovery 81098d59-1894-45fd-92e5-9903b66fc2a8
relation.isOrgUnitOfPublication 665d3039-05f8-4a25-9a3c-b9550bffecef
relation.isOrgUnitOfPublication 52f507ab-f278-4a1f-824c-44da2a86bd51
relation.isOrgUnitOfPublication ef13a800-4c99-4124-81e0-3e25b33c0c2b
relation.isOrgUnitOfPublication.latestForDiscovery 665d3039-05f8-4a25-9a3c-b9550bffecef

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
1-s2.0-S0169260713003829-main.pdf
Size:
1.61 MB
Format:
Adobe Portable Document Format
Description:
Makale Dosyası

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.44 KB
Format:
Item-specific license agreed upon to submission
Description: