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Recognition of Consumer Preference by Analysis and Classification EEG Signals
Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection sys...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838383/ https://www.ncbi.nlm.nih.gov/pubmed/33519402 http://dx.doi.org/10.3389/fnhum.2020.604639 |
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author | Aldayel, Mashael Ykhlef, Mourad Al-Nafjan, Abeer |
author_facet | Aldayel, Mashael Ykhlef, Mourad Al-Nafjan, Abeer |
author_sort | Aldayel, Mashael |
collection | PubMed |
description | Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. |
format | Online Article Text |
id | pubmed-7838383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78383832021-01-28 Recognition of Consumer Preference by Analysis and Classification EEG Signals Aldayel, Mashael Ykhlef, Mourad Al-Nafjan, Abeer Front Hum Neurosci Human Neuroscience Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. Frontiers Media S.A. 2021-01-13 /pmc/articles/PMC7838383/ /pubmed/33519402 http://dx.doi.org/10.3389/fnhum.2020.604639 Text en Copyright © 2021 Aldayel, Ykhlef and Al-Nafjan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Human Neuroscience Aldayel, Mashael Ykhlef, Mourad Al-Nafjan, Abeer Recognition of Consumer Preference by Analysis and Classification EEG Signals |
title | Recognition of Consumer Preference by Analysis and Classification EEG Signals |
title_full | Recognition of Consumer Preference by Analysis and Classification EEG Signals |
title_fullStr | Recognition of Consumer Preference by Analysis and Classification EEG Signals |
title_full_unstemmed | Recognition of Consumer Preference by Analysis and Classification EEG Signals |
title_short | Recognition of Consumer Preference by Analysis and Classification EEG Signals |
title_sort | recognition of consumer preference by analysis and classification eeg signals |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7838383/ https://www.ncbi.nlm.nih.gov/pubmed/33519402 http://dx.doi.org/10.3389/fnhum.2020.604639 |
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