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Feature selection of EEG signals in neuromarketing
Brain–computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138093/ https://www.ncbi.nlm.nih.gov/pubmed/35634118 http://dx.doi.org/10.7717/peerj-cs.944 |
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author | Al-Nafjan, Abeer |
author_facet | Al-Nafjan, Abeer |
author_sort | Al-Nafjan, Abeer |
collection | PubMed |
description | Brain–computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain’s responses to marketing stimuli. This study sought to detect two preference states (like and dislike) in EEG neuromarketing data using the proposed EEG-based consumer preference recognition system. This study investigated the role of feature selection in BCI to improve the accuracy of preference detection for neuromarketing. Several feature selection methods were used for benchmark testing in multiple BCI studies. Four feature selection approaches, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), recursive feature elimination (RFE), and ReliefF, were used with five different classifiers: deep neural network (DNN), support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest (RF). The four approaches were compared to evaluate the importance of feature selection. Moreover, the performance of classification algorithms was evaluated before and after feature selection. It was found that feature selection for EEG signals improves the performance of all classifiers. |
format | Online Article Text |
id | pubmed-9138093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91380932022-05-28 Feature selection of EEG signals in neuromarketing Al-Nafjan, Abeer PeerJ Comput Sci Bioinformatics Brain–computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain’s responses to marketing stimuli. This study sought to detect two preference states (like and dislike) in EEG neuromarketing data using the proposed EEG-based consumer preference recognition system. This study investigated the role of feature selection in BCI to improve the accuracy of preference detection for neuromarketing. Several feature selection methods were used for benchmark testing in multiple BCI studies. Four feature selection approaches, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), recursive feature elimination (RFE), and ReliefF, were used with five different classifiers: deep neural network (DNN), support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest (RF). The four approaches were compared to evaluate the importance of feature selection. Moreover, the performance of classification algorithms was evaluated before and after feature selection. It was found that feature selection for EEG signals improves the performance of all classifiers. PeerJ Inc. 2022-04-26 /pmc/articles/PMC9138093/ /pubmed/35634118 http://dx.doi.org/10.7717/peerj-cs.944 Text en © 2022 Al-Nafjan https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Al-Nafjan, Abeer Feature selection of EEG signals in neuromarketing |
title | Feature selection of EEG signals in neuromarketing |
title_full | Feature selection of EEG signals in neuromarketing |
title_fullStr | Feature selection of EEG signals in neuromarketing |
title_full_unstemmed | Feature selection of EEG signals in neuromarketing |
title_short | Feature selection of EEG signals in neuromarketing |
title_sort | feature selection of eeg signals in neuromarketing |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138093/ https://www.ncbi.nlm.nih.gov/pubmed/35634118 http://dx.doi.org/10.7717/peerj-cs.944 |
work_keys_str_mv | AT alnafjanabeer featureselectionofeegsignalsinneuromarketing |