International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 258-270
Published online September 25, 2024
https://doi.org/10.5391/IJFIS.2024.24.3.258
© The Korean Institute of Intelligent Systems
Nasim Alnuman1,2, Samira Al-Nasser1, and Omar Yasin1
1Department of Biomedical Engineering, German-Jordanian University, Amman, Jordan
2School of Allied Medical Sciences, Isra University, Amman, Jordan
Correspondence to :
Nasim Alnuman (Nasim.alnuman@gju.edu.jo)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Brain activity analysis during the visualization of different commercial images can help better understand brain activities and their application in neuromarketing. This study evaluates different electroencephalography (EEG) time- and frequency-domain features within different brain regions with six different classifiers, namely, k-nearest neighbors, pseudo-quadratic discriminant analysis, naïve Bayes, support vector machine (SVM), random forest (RF), and decision tree, to determine the best features and brain regions associated with decision-making. An online dataset of 25 users’ responses to 42 products using a 14-channel EEG system was used. The outputs included two classes: like and dislike. Twenty-one features were derived from the preprocessed data using a window size of 1 s for 4 s for the EEG signals. The best-performing classifiers were SVM and RF, and the best features were Willison amplitude (66.9%) and Hjorth complexity (66.3%) using all channels. Furthermore, the temporal and frontal lobes of the brain showed higher accuracy than other regions, and the right frontal lobe was more dominant than the left frontal lobe in relation to product preference decisions and displayed the potential to classify users’ decisions for future simplified systems.
Keywords: Neuromarketing, Product preference, EEG, Classification
No potential conflict of interest relevant to this article was reported.
International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 258-270
Published online September 25, 2024 https://doi.org/10.5391/IJFIS.2024.24.3.258
Copyright © The Korean Institute of Intelligent Systems.
Nasim Alnuman1,2, Samira Al-Nasser1, and Omar Yasin1
1Department of Biomedical Engineering, German-Jordanian University, Amman, Jordan
2School of Allied Medical Sciences, Isra University, Amman, Jordan
Correspondence to:Nasim Alnuman (Nasim.alnuman@gju.edu.jo)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Brain activity analysis during the visualization of different commercial images can help better understand brain activities and their application in neuromarketing. This study evaluates different electroencephalography (EEG) time- and frequency-domain features within different brain regions with six different classifiers, namely, k-nearest neighbors, pseudo-quadratic discriminant analysis, naïve Bayes, support vector machine (SVM), random forest (RF), and decision tree, to determine the best features and brain regions associated with decision-making. An online dataset of 25 users’ responses to 42 products using a 14-channel EEG system was used. The outputs included two classes: like and dislike. Twenty-one features were derived from the preprocessed data using a window size of 1 s for 4 s for the EEG signals. The best-performing classifiers were SVM and RF, and the best features were Willison amplitude (66.9%) and Hjorth complexity (66.3%) using all channels. Furthermore, the temporal and frontal lobes of the brain showed higher accuracy than other regions, and the right frontal lobe was more dominant than the left frontal lobe in relation to product preference decisions and displayed the potential to classify users’ decisions for future simplified systems.
Keywords: Neuromarketing, Product preference, EEG, Classification
EEG electrodes placement and brain regions [
Images displayed to the subjects [
EEG preprocessing procedure.
Classifier performance for time-domain features.
Classifier performance for frequency-domain features.
Regions of the brain: RF performance on frequency-domain features.
Frontal lobe: RF performance on frequency-domain features.
Sample confusion matrix for theWAMP feature using the RF classifier.
Nishant Chauhan and Byung-Jae Choi
International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(1): 10-18 https://doi.org/10.5391/IJFIS.2024.24.1.10Nishant Chauhan and Byung-Jae Choi
International Journal of Fuzzy Logic and Intelligent Systems 2021; 21(4): 349-357 https://doi.org/10.5391/IJFIS.2021.21.4.349Jihad Anwar Qadir, Abdulbasit K. Al-Talabani, and Hiwa A. Aziz
International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(4): 272-277 https://doi.org/10.5391/IJFIS.2020.20.4.272EEG electrodes placement and brain regions [
Images displayed to the subjects [
EEG preprocessing procedure.
|@|~(^,^)~|@|Classifier performance for time-domain features.
|@|~(^,^)~|@|Classifier performance for frequency-domain features.
|@|~(^,^)~|@|Regions of the brain: RF performance on frequency-domain features.
|@|~(^,^)~|@|Frontal lobe: RF performance on frequency-domain features.
|@|~(^,^)~|@|Sample confusion matrix for theWAMP feature using the RF classifier.