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Khondakar MFK, Sarowar MH, Chowdhury MH, Majumder S, Hossain MA, Dewan MAA, Hossain QD. A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain Inform 2024; 11:17. [PMID: 38837089 PMCID: PMC11153447 DOI: 10.1186/s40708-024-00229-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/25/2024] [Indexed: 06/06/2024] Open
Abstract
Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.
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Affiliation(s)
- Md Fazlul Karim Khondakar
- Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Md Hasib Sarowar
- Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Mehdi Hasan Chowdhury
- Department of Electrical & Electronic Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh.
| | - Sumit Majumder
- Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Md Azad Hossain
- Department of Electronics & Telecommunication Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - M Ali Akber Dewan
- School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB, T9S 3A3, Canada
| | - Quazi Delwar Hossain
- Department of Electrical & Electronic Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
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Safari M, Shalbaf R, Bagherzadeh S, Shalbaf A. Classification of mental workload using brain connectivity and machine learning on electroencephalogram data. Sci Rep 2024; 14:9153. [PMID: 38644365 PMCID: PMC11033270 DOI: 10.1038/s41598-024-59652-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 04/12/2024] [Indexed: 04/23/2024] Open
Abstract
Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).
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Affiliation(s)
| | - Reza Shalbaf
- Institute for Cognitive Science Studies, Tehran, Iran.
| | - Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Nam H, Kim JM, Choi W, Bak S, Kam TE. The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets. Front Hum Neurosci 2023; 17:1205881. [PMID: 37342822 PMCID: PMC10277566 DOI: 10.3389/fnhum.2023.1205881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Introduction The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. Methods In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. Results and discussion The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.
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Affiliation(s)
| | | | | | | | - Tae-Eui Kam
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
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Shah SMA, Usman SM, Khalid S, Rehman IU, Anwar A, Hussain S, Ullah SS, Elmannai H, Algarni AD, Manzoor W. An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:9744. [PMID: 36560113 PMCID: PMC9782208 DOI: 10.3390/s22249744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/21/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
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.
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Affiliation(s)
- Syed Mohsin Ali Shah
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan
| | - Syed Muhammad Usman
- Department of Creative Technologies, Faculty of Computing and AI, Air University, Islamabad 44000, Pakistan
| | - Shehzad Khalid
- Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan
| | - Ikram Ur Rehman
- School of Computing and Engineering, The University of West London, London W5 5RF, UK
| | - Aamir Anwar
- School of Computing and Engineering, The University of West London, London W5 5RF, UK
| | - Saddam Hussain
- School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
| | - Syed Sajid Ullah
- Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abeer D. Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Waleed Manzoor
- Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan
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Byrne A, Bonfiglio E, Rigby C, Edelstyn N. A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research. Brain Inform 2022; 9:27. [PMCID: PMC9663791 DOI: 10.1186/s40708-022-00175-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Abstract
Introduction
The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.
Methods
Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.
Results
Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.
Conclusions and implications
FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.
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