A Proposed LSTM‐Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG

Gill, Rupali; Singh, Jaiteg

doi:10.1002/9781119792437.ch8

Cited by 9 publications

(4 citation statements)

References 100 publications

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“…Aldayel et al [4] suggested a consumer choice recognition system with an average sensitivity of 91.2%, but only 81.5% specificity. Rupali et al [57] and Yadava et al [1] have employed DWT for feature extraction, but the proposed architecture uses automated as well as handcrafted characteristics to classify between likes and dislikes. The proposed method has obtained a sensitivity of 95.89% while maintaining a specificity of 96.21%.…”

Section: Evaluation Of the Effectiveness Of The Proposed Methods In C...mentioning

confidence: 99%

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Shah

Usman

Khalid

et al. 2022

Sensors

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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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Section: Evaluation Of the Effectiveness Of The Proposed Methods In C...mentioning

confidence: 99%

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Shah

Usman

Khalid

et al. 2022

Sensors

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“…In light of this, we utilised the SNS user's post pictures and hashtags to categorise their feelings [30]. Since the influence of SNS platforms on cybersecurity is substantial, another study analysed images shared on SNS and categorised the SNS platforms to which the images were shared [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized recognition system in online shopping by using deep learning

Devarakonda Venkata,

Donda,

Madhavi

et al. 2024

EAI Endorsed Trans IoT

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This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.

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“…A EEG tem validade interna e confiabilidade e é uma ferramenta já validada na literatura (BULLY; ADU-BROBBEY, DUODU, 2016). A EEG é utilizada em pesquisas da neurociência e neurociência aplicada ao consumo para avaliar as preferências, a motivação, a atenção, a memória, o processo de decisão (gostar ou não gostar), o estado emocional dos consumidores, dentre outros (BALDO et al, 2022;GILL;SINGH, 2022;NIZAM et al, 2022;PAGAN et al, 2021b;RAMIREZ et al, 2022;YASSIN et al, 2022). Ela foi escolhida para ser trabalhada nesta pesquisa, pois é importante conhecer as respostas do processamento cognitivo, da valência emocional e das preferências de regiões cerebrais superficiais, visto que, a literatura existente foca mais na avaliação de regiões cerebrais profundas com o uso da fRMI (NAZARI et al, 2004;SCHAERFER, 2006;SCHAERFER;ROT, 2007REIMANN et al, 2012…”

Section: Estas Pesquisas Analisaram O Processamento Cognitivo (Ativaç...unclassified

Avaliação do processamento cognitivo, da valência emocional e das preferências de consumidores em relação às marcas de cervejas por meio da abordagem da neurociência aplicada ao consumo: um estudo sobre gênero, envolvimento e eletroencefalografia (EEG)

Pagan¹

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Agradeço a Deus por tudo, a Jesus e a Virgem Maria. À minha irmã, Karina por estar sempre comigo, seja nas atividades da pós-graduação, seja na cotidiana. Aos meus pais, Rita e Paulo por acreditarem em mim e estarem presente em nossas vidas dando incentivo e apoio. A minha tia Maria Inês pelo companheirismo, apoio e carinho. Aos meus avôs e minhas avós por cuidarem de mim quando criança e por terem me ensinado bons valores. As minhas tias, tios, primas, primos, madrinhas e padrinho pela amizade. Às minhas amigas: Mariana e Juliana. Às minhas primas Ingrid, Amanda e Vanilde pelo companheirismo. E aos meus amigos e amigas do grupo de pesquisa G-mind.

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A Proposed LSTM‐Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG

Cited by 9 publications

References 100 publications

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Personalized recognition system in online shopping by using deep learning

Avaliação do processamento cognitivo, da valência emocional e das preferências de consumidores em relação às marcas de cervejas por meio da abordagem da neurociência aplicada ao consumo: um estudo sobre gênero, envolvimento e eletroencefalografia (EEG)

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