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Yuan G, Wang T, Ju W, Fu S. A portable affective computing system for identifying mate preference. Sci Rep 2024; 14:17735. [PMID: 39085370 PMCID: PMC11292018 DOI: 10.1038/s41598-024-68772-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024] Open
Abstract
Recognizing an individual's preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology's practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction. To achieve this goal, we first conducted an interesting simulated dating experiment to collect the necessary data. Next, EEG features were extracted from various dimensions, including band power and asymmetry index features. Then, we introduced a novel method for EEG feature and channel selection that combines the sequential forward selection (SFS) algorithm with the frequency-based feature subset integration (FFSI) algorithm. Finally, we used the random forest classifier (RFC) to determine a person's preference state for potential romantic partners. Experimental results indicate that the optimal feature subset, selected using the SFS-FFSI method, attained an average classification accuracy of 88.42%. Notably, these features were predominantly sourced from asymmetry index features of electrodes situated in the frontal, parietal, and occipital lobes.
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Affiliation(s)
- Guangjie Yuan
- School of Psychology, Qufu Normal University, Shandong, China.
- College of Electronic and Information Engineering, Southwest University, Chongqing, China.
| | - Tao Wang
- School of Psychology, Qufu Normal University, Shandong, China
| | - Wei Ju
- School of Psychology, Qufu Normal University, Shandong, China
| | - Sai Fu
- Faculty of Education, Southwest University, Chongqing, China.
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2
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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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3
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Zhang H, Yin L, Zhang H. Using subjective emotion, facial expression, and gaze direction to evaluate user affective experience and predict preference when playing single-player games. ERGONOMICS 2024:1-21. [PMID: 38832783 DOI: 10.1080/00140139.2024.2359123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 05/18/2024] [Indexed: 06/05/2024]
Abstract
The affective experience generated when users play computer games can influence their attitude and preference towards the game. Existing evaluation means mainly depend on subjective scales and physiological signals. However, some limitations should not be ignored (e.g. subjective scales are not objective, and physiological signals are complicated). In this paper, we 1) propose a novel method to assess user affective experience when playing single-player games based on pleasure-arousal-dominance (PAD) emotions, facial expressions, and gaze directions, and 2) build an artificial intelligence model to identify user preference. Fifty-four subjects participated in a basketball experiment with three difficulty levels. Their expressions, gaze directions, and subjective PAD emotions were collected and analysed. Experimental results showed that the expression intensities of angry, sad, and neutral, yaw angle degrees of gaze direction, and PAD emotions varied significantly under different difficulties. Besides, the proposed model achieved better performance than other machine-learning algorithms on the collected dataset.
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Affiliation(s)
- He Zhang
- School of Design, Hunan University, Changsha, China
| | - Lu Yin
- School of Design, Hunan University, Changsha, China
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4
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Yu D, Wei H, Xuefeng Z, Zhongxuan H, Yijun Z. The effect of new e-commerce platform's OSC promotion on consumer cognition: from cognitive legitimacy and cognitive psychology perspective. Front Hum Neurosci 2024; 18:1380259. [PMID: 38873655 PMCID: PMC11169696 DOI: 10.3389/fnhum.2024.1380259] [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: 02/01/2024] [Accepted: 05/20/2024] [Indexed: 06/15/2024] Open
Abstract
Introduction In the realm of emerging e-commerce platforms, the influence of online shopping events, specifically online carnival promotions (OSC), on consumer behavior is a significant area of interest.This paper delves into the effects of such promotions on consumer perceptions, a topic that has not been extensively explored in academic research. Methods To investigate this phenomenon, two distinct studies were conducted. The first study employed a questionnaire-based experiment involving 220 participants, divided into two groups. The first study examined the mediating role of cognitive legitimacy in the relationship between OSC events organized by new e-commerce platforms and the perceptions of consumers. The second study utilized an event-related potentials (ERPs) experiment with 33 participants to explore the differences in consumer perceptions between OSC promotions and general promotions by new e-commerce platforms. This study measured the brain's response to promotional stimuli to gain insights into the cognitive processes involved. Results The first study yielded results that suggest OSC activities can facilitate the establishment of cognitive legitimacy for new e-commerce platforms. This, in turn, was found to be associated with an increase in positive purchase intentions among consumers. In the second study, the ERPs data indicated that exposure to OSC promotional materials elicited larger P2 and N2 ERP components when participants were presented with the logo of a new e-commerce platform. This was in contrast to the response to general promotional materials, suggesting a heightened cognitive and perceptual engagement with OSC promotions. Discussion The findings from both studies collectively imply that OSC promotions have a distinct impact on consumer perceptions and cognitive processes. The implicit memory triggered by these promotions appears to influence the identification of new platforms and the mechanisms of cognitive control during online shopping. This, in turn, may have implications for explicit consumer behavior, suggesting that OSC promotions could be a powerful tool for shaping consumer attitudes and behaviors in the e-commerce space. The results underscore the importance of understanding the nuances of consumer engagement with new e-commerce platforms and the role of promotional strategies in fostering a positive brand image and consumer loyalty.
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Affiliation(s)
| | | | - Zhang Xuefeng
- School of Management, Southwest University of Political Science and Law, Chongqing, China
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5
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Panteli A, Kalaitzi E, Fidas CA. A review on the use of eeg for the investigation of the factors that affect Consumer's behavior. Physiol Behav 2024; 278:114509. [PMID: 38485039 DOI: 10.1016/j.physbeh.2024.114509] [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: 11/02/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/20/2024]
Abstract
This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer's behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals' acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers' choices, but also those that studied consumers' decisions as a result of the interactions that take place among the received marketing messages and the individual's internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer's behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied.
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Affiliation(s)
- Antiopi Panteli
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece.
| | - Eirini Kalaitzi
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
| | - Christos A Fidas
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
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Marques dos Santos JP, Marques dos Santos JD. Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception. Front Hum Neurosci 2024; 18:1305164. [PMID: 38584851 PMCID: PMC10995351 DOI: 10.3389/fnhum.2024.1305164] [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: 09/30/2023] [Accepted: 03/04/2024] [Indexed: 04/09/2024] Open
Abstract
Introduction The research in consumer neuroscience has identified computational methods, particularly artificial intelligence (AI) and machine learning, as a significant frontier for advancement. Previously, we utilized functional magnetic resonance imaging (fMRI) and artificial neural networks (ANNs) to model brain processes related to brand preferences in a paradigm exempted from motor actions. In the current study, we revisit this data, introducing recent advancements in explainable artificial intelligence (xAI) to gain insights into this domain. By integrating fMRI data analysis, machine learning, and xAI, our study aims to search for functional brain networks that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands, focusing on the early processing stages. Methods We applied independent component analysis (ICA) to overcome the expected fMRI data's high dimensionality, which raises hurdles in AI applications. We extracted pertinent features from the returned ICs. An ANN is then trained on this data, followed by pruning and retraining processes. We then apply explanation techniques, based on path-weights and Shapley values, to make the network more transparent, explainable, and interpretable, and to obtain insights into the underlying brain processes. Results The fully connected ANN model obtained an accuracy of 54.6%, which dropped to 50.4% after pruning. However, the retraining process allowed it to surpass the fully connected network, achieving an accuracy of 55.9%. The path-weights and Shapley-based analysis concludes that, regarding brand perception, the expected initial participation of the primary visual system is followed. Other brain areas participate in early processing and discriminate between preferred and indifferent brands, such as the cuneal and the lateral occipital cortices. Discussion The most important finding is that a split between processing brands|preferred from brands|indifferent may occur during early processing stages, still in the visual system. However, we found no evidence of a "decision pipeline" that would yield if a brand is preferred or indifferent. The results suggest the existence of a "tagging"-like process in parallel flows in the extrastriate. Network training dynamics aggregate specific processes within the hidden nodes by analyzing the model's hidden layer. This yielded that some nodes contribute to both global brand appraisal and specific brand category classification, shedding light on the neural substrates of decision-making in response to brand stimuli.
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Affiliation(s)
- José Paulo Marques dos Santos
- Department of Business Administration, University of Maia, Maia, Portugal
- Unit of Experimental Biology, Faculty of Medicine, University of Porto, Porto, Portugal
- LIACC – Artificial Intelligence and Computer Science Laboratory, University of Porto, Porto, Portugal
- NECE-UBI, Research Centre for Business Sciences, University of Beira Interior, Covilhã, Portugal
| | - José Diogo Marques dos Santos
- Faculty of Engineering, University of Porto, Porto, Portugal
- Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal
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7
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Aldayel M, Al-Nafjan A. A comprehensive exploration of machine learning techniques for EEG-based anxiety detection. PeerJ Comput Sci 2024; 10:e1829. [PMID: 38435618 PMCID: PMC10909191 DOI: 10.7717/peerj-cs.1829] [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: 08/17/2023] [Accepted: 12/29/2023] [Indexed: 03/05/2024]
Abstract
The performance of electroencephalogram (EEG)-based systems depends on the proper choice of feature extraction and machine learning algorithms. This study highlights the significance of selecting appropriate feature extraction and machine learning algorithms for EEG-based anxiety detection. We explored different annotation/labeling, feature extraction, and classification algorithms. Two measurements, the Hamilton anxiety rating scale (HAM-A) and self-assessment Manikin (SAM), were used to label anxiety states. For EEG feature extraction, we employed the discrete wavelet transform (DWT) and power spectral density (PSD). To improve the accuracy of anxiety detection, we compared ensemble learning methods such as random forest (RF), AdaBoost bagging, and gradient bagging with conventional classification algorithms including linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers. We also evaluated the performance of the classifiers using different labeling (SAM and HAM-A) and feature extraction algorithms (PSD and DWT). Our findings demonstrated that HAM-A labeling and DWT-based features consistently yielded superior results across all classifiers. Specifically, the RF classifier achieved the highest accuracy of 87.5%, followed by the Ada boost bagging classifier with an accuracy of 79%. The RF classifier outperformed other classifiers in terms of accuracy, precision, and recall.
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Affiliation(s)
- Mashael Aldayel
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Abeer Al-Nafjan
- Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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8
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Qin Y, Zhang Y, Zhang Y, Liu S, Guo X. Application and Development of EEG Acquisition and Feedback Technology: A Review. BIOSENSORS 2023; 13:930. [PMID: 37887123 PMCID: PMC10605290 DOI: 10.3390/bios13100930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/05/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023]
Abstract
This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain-computer interface technology utilizes EEG signals for human-computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain-computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.
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Affiliation(s)
- Yong Qin
- Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China;
| | - Yanpeng Zhang
- Beijing Perfect-Protection Technology Co., Ltd., Beijing 101601, China; (Y.Z.); (Y.Z.); (S.L.)
| | - Yan Zhang
- Beijing Perfect-Protection Technology Co., Ltd., Beijing 101601, China; (Y.Z.); (Y.Z.); (S.L.)
| | - Sheng Liu
- Beijing Perfect-Protection Technology Co., Ltd., Beijing 101601, China; (Y.Z.); (Y.Z.); (S.L.)
| | - Xiaogang Guo
- Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China;
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9
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Al-Nafjan A, Aldayel M, Kharrat A. Systematic Review and Future Direction of Neuro-Tourism Research. Brain Sci 2023; 13:brainsci13040682. [PMID: 37190647 DOI: 10.3390/brainsci13040682] [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: 03/26/2023] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists' conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain-computer interface (BCI), eye-tracking, galvanic skin response, etc., to create tourism goods and services to improve tourist experience and satisfaction. Due to the novelty of neuro-tourism and the dearth of studies on this subject, this study offered a comprehensive analysis of the peer-reviewed journal publications in neuro-tourism research for the previous 12 years to detect trends in this field and provide insights for academics. We reviewed 52 articles indexed in the Web of Science (WoS) core collection database and examined them using our suggested classification schema. The results reveal a large growth in the number of published articles on neuro-tourism, demonstrating a rise in the relevance of this field. Additionally, the findings indicated a lack of integrating artificial intelligence techniques in neuro-tourism studies. We believe that the advancements in technology and research collaboration will facilitate exponential growth in this field.
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Affiliation(s)
- Abeer Al-Nafjan
- Computer Science Department, College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
| | - Mashael Aldayel
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Amira Kharrat
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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10
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Ding N, Zhong Y, Li J, Xiao Q, Zhang S, Xia H. Visual preference of plant features in different living environments using eye tracking and EEG. PLoS One 2022; 17:e0279596. [PMID: 36584138 PMCID: PMC9803246 DOI: 10.1371/journal.pone.0279596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 12/12/2022] [Indexed: 12/31/2022] Open
Abstract
Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people's preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people's visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions.
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Affiliation(s)
- Ningning Ding
- Central South University of Forestry and Technology, Changsha, China
| | - Yongde Zhong
- Central South University of Forestry and Technology, Changsha, China,National Forestry and Grassland Administration State Forestry Administration Engineering Research Center for Forest Tourism, Changsha, China,* E-mail:
| | - Jiaxiang Li
- Central South University of Forestry and Technology, Changsha, China
| | - Qiong Xiao
- Central South University of Forestry and Technology, Changsha, China
| | - Shuangquan Zhang
- Central South University of Forestry and Technology, Changsha, China
| | - Hongling Xia
- Hunan Urban Construction College, Xiangtan, China
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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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Mashrur FR, Rahman KM, Miya MTI, Vaidyanathan R, Anwar SF, Sarker F, Mamun KA. BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework. Front Hum Neurosci 2022; 16:861270. [PMID: 35693537 PMCID: PMC9177951 DOI: 10.3389/fnhum.2022.861270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.
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Affiliation(s)
- Fazla Rabbi Mashrur
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Institute for Advanced Research (IAR), United International University, Dhaka, Bangladesh
| | | | | | - Ravi Vaidyanathan
- Department of Mechanical Engineering and UK Dementia Research Institute Care, Research and Technology Centre (DRI-CR&T), Imperial College London, London, United Kingdom
| | - Syed Ferhat Anwar
- Institute of Business Administration, University of Dhaka, Dhaka, Bangladesh
| | - Farhana Sarker
- Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh
| | - Khondaker A. Mamun
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Institute for Advanced Research (IAR), United International University, Dhaka, Bangladesh
- Department of Computer Science & Engineering, United International University, Dhaka, Bangladesh
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Al-Nafjan A. Feature selection of EEG signals in neuromarketing. PeerJ Comput Sci 2022; 8:e944. [PMID: 35634118 PMCID: PMC9138093 DOI: 10.7717/peerj-cs.944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/16/2022] [Indexed: 06/15/2023]
Abstract
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.
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Hei Y, Yuan T, Fan Z, Yang B, Hu J. Sleep staging classification based on a new parallel fusion method of multiple sources signals. Physiol Meas 2022; 43. [PMID: 35381584 DOI: 10.1088/1361-6579/ac647b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/05/2022] [Indexed: 11/12/2022]
Abstract
APPROACH First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm (WCA) and an "HYF" RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform (DWT), Autoregressive (AR), and Power Spectral entropy (PSE), and Refined Composite Multiscale Dispersion Entropy (RCMDE). Third, a new "Parallel Fusion Method" (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classification models is employed for sleep staging. MAIN RESULTS Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is testedby evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 82.7% and the kappa coefficient is 0.711, also by using SVM classification model with the PFM is 83.7%, and the kappa coefficient is 0.724. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved. Significance: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.
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Affiliation(s)
- Yafang Hei
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, Shuangliu, Chengdu, Chengdu, Sichuan, 610225, CHINA
| | - Tuming Yuan
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, Chengdu, Sichuan, 610225, CHINA
| | - Zhigao Fan
- School of Atmospheric Sciences, Chengdu University of Information Technology, Xuefu road 24, Chengdu, Sichuan, 610225, CHINA
| | - Bo Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Xuefu road 24, , , 610225, CHINA
| | - Jiancheng Hu
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, , , 610225, CHINA
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Yuan G, He W, Liu G. Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction. Front Neurosci 2022; 16:830820. [PMID: 35221907 PMCID: PMC8873380 DOI: 10.3389/fnins.2022.830820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Initial romantic attraction (IRA) refers to a series of positive reactions toward potential ideal partners based on individual preferences; its evolutionary value lies in facilitating mate selection. Although the EEG activities associated with IRA have been preliminarily understood; however, it remains unclear whether IRA can be recognized based on EEG activity. To clarify this, we simulated a dating platform similar to Tinder. Participants were asked to imagine that they were using the simulated dating platform to choose the ideal potential partner. Their brain electrical signals were recorded as they viewed photos of each potential partner and simultaneously assessed their initial romantic attraction in that potential partner through self-reported scale responses. Thereafter, the preprocessed EEG signals were decomposed into power-related features of different frequency bands using a wavelet transform approach. In addition to the power spectral features, feature extraction also accounted for the physiological parameters related to hemispheric asymmetries. Classification was performed by employing a random forest classifier, and the signals were divided into two categories: IRA engendered and IRA un-engendered. Based on the results of the 10-fold cross-validation, the best classification accuracy 85.2% (SD = 0.02) was achieved using feature vectors, mainly including the asymmetry features in alpha (8–13 Hz), beta (13–30 Hz), and theta (4–8 Hz) rhythms. The results of this study provide early evidence for EEG-based mate preference recognition and pave the way for the development of EEG-based romantic-matching systems.
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Affiliation(s)
- Guangjie Yuan
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
| | - Wenguang He
- College of Psychology, Qufu Normal University, Qufu, China
| | - Guangyuan Liu
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- *Correspondence: Guangyuan Liu,
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Zeng L, Lin M, Xiao K, Wang J, Zhou H. Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing. Front Hum Neurosci 2022; 15:793952. [PMID: 35069157 PMCID: PMC8770276 DOI: 10.3389/fnhum.2021.793952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/26/2021] [Indexed: 12/03/2022] Open
Abstract
Neuromarketing is an emerging research field for prospective businesses on consumer’s preference. Consumer’s preference prediction based on electroencephalography (EEG) can reliably predict likes or dislikes of a product. However, the current EEG prediction and classification accuracy have yet to reach ideal level. In addition, it is still unclear how different brain region information and different features such as power spectral density, brain asymmetry, differential entropy, and Hjorth parameters affect the prediction accuracy. Our study shows that by taking footwear products as an example, the recognition accuracy of product likes or dislikes reaches 94.22%. Compared with other brain regions, the features of the frontal and occipital brain region obtained a higher prediction accuracy, but the fusion of the features of the whole brain region could improve the prediction accuracy of likes or dislikes even further. Future work would be done to correlate the EEG-based like or dislike prediction results with product sales and self-reports.
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Affiliation(s)
- Li Zeng
- School of Business, Hohai University, Nanjing, China
- College of Environment, Hohai University, Nanjing, China
| | - Mengsi Lin
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Keyang Xiao
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
| | - Jigan Wang
- School of Business, Hohai University, Nanjing, China
| | - Hui Zhou
- School of Automation, Nanjing University of Science and Technology, Nanjing, China
- *Correspondence: Hui Zhou,
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Abstract
Epileptic diseases take EEG as an important basis for clinical judgment, and fractal algorithms were often used to analyze electroencephalography (EEG) signals. However, the variation trends of fractal dimension (D) were opposite in the literature, i.e., both D decreasing and increasing were reported in previous studies during seizure status relative to the normal status, undermining the feasibility of fractal algorithms for EEG analysis to detect epileptic seizures. In this study, two algorithms with high accuracy in the D calculation, Higuchi and roughness scaling extraction (RSE), were used to study D variation of EEG signals with seizures. It was found that the denoising operation had an important influence on D variation trend. Moreover, the D variation obtained by RSE algorithm was larger than that by Higuchi algorithm, because the non-fractal nature of EEG signals during normal status could be detected and quantified by RSE algorithm. The above findings in this study could be promising to make more understandings of the nonlinear nature and scaling behaviors of EEG signals.
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