Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autor principal: Al-Nafjan, Abeer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
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
_version_ 1784714540623069184
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