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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...

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Autores principales: Aldayel, Mashael, Ykhlef, Mourad, Al-Nafjan, Abeer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
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.
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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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