Developing a Web Application for Recognizing Emotions in Neuromarketing

Filipović, Filip; Baljak, Luka; Naumović, Tamara; Лабус, Александра; Bogdanović, Zorica

doi:10.1007/978-981-15-1564-4_28

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…In the last 20 years, researchers proposed several automatic approaches with some of these considering the neurological mechanisms that drive marketing decision-making and contribute to the rapidly expanding field of neuromarketing research. In neuromarketing studies, researchers use biometric responses such as facial expression (Filipović et al, 2020 ), eye tracking (Khushaba et al, 2013 ), functional magnetic resonance imaging (fMRI) (Hsu and Cheng, 2018 ), galvanic skin response (Ohira and Hirao, 2015 ), and electroencephalography (EEG) (Golnar-Nik et al, 2019 ), magnetoencephalograpy (MEG) (Hege et al, 2014 ) to extract customers' insights. Previously, the neuromarketing community was primarily interested in fMRI, which assesses cerebral blood flow imaging and aids in the identification of areas triggered by stimuli (Rawnaque et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Mashrur

Rahman

Miya

et al. 2022

Front. Hum. Neurosci.

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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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Section: Introductionmentioning

confidence: 99%

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Mashrur

Rahman

Miya

et al. 2022

Front. Hum. Neurosci.

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“…Some studies also seek to understand how the visual attention and the attitude of consumers can be impacted by the advertisements (Cartocci et al , 2016; Vecchiato et al , 2014; Filipović et al , 2020). In context to this, attitude is the representation of a person's assessment of an entity in question (Ajzen and Fishbein, 1977).…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Gender differences: visual attention and attitude toward advertisements

Boscolo

Oliveira

Maheshwari

et al. 2020

MIP

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PurposeThis study examines the differences between genders in visual attention and attitudes toward different types of advertisements.Design/methodology/approachAn experimental design using a structured questionnaire and six print advertisements with a male, female and neutral focus was used to evaluate gender differences. In total, 180 students from a public university in Brazil participated in the study. An eye-tracking device was employed, using the Tobii Studio software, to get the visual attention metrics for this study.FindingsIn the case of the female advertisements, no significant difference between visual attention and attitude was found; however, differences were found in the case of male visual attention to the image and their relative attitudes toward the advertisements.Research limitations/implicationsBecause it is a laboratory experiment using quota sampling, mainly Latin consumers, the potential for broader generalization may be limited. Besides, since they are real image advertisement images, there may be some interference in the respondents' responses from previous interactions with the brand or product exposed or even from a prior observation of this advertisement.Originality/valueThis study provides deeper insight into Latin consumers' preferences and associations, who have a different cultural and national context. This study contributes to the use of the eye-tracking tool as a neuromarketing technique to evaluate and analyze visual attention.

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“…Chen et al (2015) published a paradigmatic study combining fMRI and MVPA-based (multi-voxel pattern analysis) data analysis (Mumford et al, 2012) to study brand associations in the consumers' brain under the brand personality framework (Aaker, 1997). Other recent studies have been combining machine learning data analysis methods with neuroscientific or biometric acquisition methods, such as facial coding (Filipović et al, 2020), electroencephalography (EEG) for preference detection (Aldayel et al, 2021), in this case, comparing distinct methods for feature extraction and classification, labeling with "buy" and "not buy" and testing with an ensemble classifier over EEG data (Georgiadis et al, 2022), or using support vector machine (SVM) over t-statistics fMRI images to study the more effective way to present apparel goods in an online shop (Jai et al, 2021). The approach in the present study is to re-analyze fMRI data searching for functional brain neural networks-because brain functions emerge from networks (Yuste, 2015)-that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands.…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception

Marques dos Santos,

Marques dos Santos

2024

Front. Hum. Neurosci.

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IntroductionThe 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.MethodsWe 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.ResultsThe 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.DiscussionThe 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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Developing a Web Application for Recognizing Emotions in Neuromarketing

Cited by 7 publications

References 4 publications

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Gender differences: visual attention and attitude toward advertisements

Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception

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