Like/dislike analysis using EEG: Determination of most discriminative channels and frequencies

dc.contributor.author Asyali, Musa H.
dc.contributor.author Gungor, Evrim
dc.contributor.author Arslan, Dilek Betul
dc.contributor.author Korkmaz, Sumeyye
dc.contributor.author Yilmaz, Bulent
dc.contributor.authorID 0000-0003-2954-1217 en_US
dc.contributor.department AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü en_US
dc.contributor.institutionauthor Yilmaz, Bulent
dc.date.accessioned 2023-08-04T06:46:19Z
dc.date.available 2023-08-04T06:46:19Z
dc.date.issued 2014 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.endpage 713 en_US
dc.identifier.issn 0169-2607
dc.identifier.issn 1872-7565
dc.identifier.issue 2 en_US
dc.identifier.other WOS:000330137600026
dc.identifier.startpage 705 en_US
dc.identifier.uri https://doi.org/10.1016/j.cmpb.2013.11.010
dc.identifier.uri https://hdl.handle.net/20.500.12573/1683
dc.identifier.volume 113 en_US
dc.language.iso eng en_US
dc.publisher ELSEVIER IRELAND LTD en_US
dc.relation.isversionof 10.1016/j.cmpb.2013.11.010 en_US
dc.relation.journal COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı 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

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