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An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications
Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782208/ https://www.ncbi.nlm.nih.gov/pubmed/36560113 http://dx.doi.org/10.3390/s22249744 |
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author | Shah, Syed Mohsin Ali Usman, Syed Muhammad Khalid, Shehzad Rehman, Ikram Ur Anwar, Aamir Hussain, Saddam Ullah, Syed Sajid Elmannai, Hela Algarni, Abeer D. Manzoor, Waleed |
author_facet | Shah, Syed Mohsin Ali Usman, Syed Muhammad Khalid, Shehzad Rehman, Ikram Ur Anwar, Aamir Hussain, Saddam Ullah, Syed Sajid Elmannai, Hela Algarni, Abeer D. Manzoor, Waleed |
author_sort | Shah, Syed Mohsin Ali |
collection | PubMed |
description | Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods. |
format | Online Article Text |
id | pubmed-9782208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97822082022-12-24 An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications Shah, Syed Mohsin Ali Usman, Syed Muhammad Khalid, Shehzad Rehman, Ikram Ur Anwar, Aamir Hussain, Saddam Ullah, Syed Sajid Elmannai, Hela Algarni, Abeer D. Manzoor, Waleed Sensors (Basel) Article Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods. MDPI 2022-12-12 /pmc/articles/PMC9782208/ /pubmed/36560113 http://dx.doi.org/10.3390/s22249744 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shah, Syed Mohsin Ali Usman, Syed Muhammad Khalid, Shehzad Rehman, Ikram Ur Anwar, Aamir Hussain, Saddam Ullah, Syed Sajid Elmannai, Hela Algarni, Abeer D. Manzoor, Waleed An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications |
title | An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications |
title_full | An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications |
title_fullStr | An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications |
title_full_unstemmed | An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications |
title_short | An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications |
title_sort | ensemble model for consumer emotion prediction using eeg signals for neuromarketing applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782208/ https://www.ncbi.nlm.nih.gov/pubmed/36560113 http://dx.doi.org/10.3390/s22249744 |
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