The impact of parameters of store illumination on food shopper response

Berčík, Jakub; Horská, Elena; Wang, Regina W.Y.; Chen, Ying‐Chun

doi:10.1016/j.appet.2016.04.010

Cited by 37 publications

(19 citation statements)

References 46 publications

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“…Because customer feelings in sales areas are strongly influenced by the perception of the surroundings, recognition of emotional responses can reveal true consumer preferences and improve and assist in the (buying) process. Four articles have presented this idea in different applications [102,123,218,280].…”

Section: Domain Description Referencesmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Domain Description Referencesmentioning

confidence: 99%

“…[102,123,218,280] Education These articles tracked students' engagement and learning. [38,281] Assistance These articles explored how assistive technologies or learning resources were provided to individuals.…”

Section: Monitoringmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…Low-pass filter 3 [22,24,38] Band-pass filters 10 [3,11,15,20,28,[39][40][41]44,53] Down sampling 3 [11,15,44] Average 7 [3,15,27,37,38,40,54]…”

Section: Number Of Studies References Preprocessingmentioning

confidence: 99%

“…Statistical features: median, standard deviation, t-test, z-score transformation 9 [3,11,24,28,35,37,38,40,41] Time-frequency analysis 2 [15,54] Wavelet domain: entropy, energy » using wavelet transform 2 [22,39] Frequency domain: power spectral density, band power, spectrum power » using Fourier transform 8 [11,27,37,39,44,[53][54][55] Correlation analysis: mutual information, correlation coefficient 3 [18,27,39] Regression/Classification [22] Linear regression (Lasso, Ridge) 1 [27] Some preference studies involved the use of more than two classification algorithms to discover well-matched classifiers for a definite feature set [12]. Chew et al [54] measured the user preferences for the aesthetics presented as virtual 3D shapes by using EEG.…”

Section: Feature Extraction Selectionmentioning

confidence: 99%

Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

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The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

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“…Uma estratégia para não modificar a rotulagem dos produtos é realizar o processo de defumação convencional pela pirólise da madeira. Nesse sentido estudar as características tanto físicoquímicas quanto sensoriais do produto defumado por madeiras de reflorestamento é indispensável, uma vez que influenciam diretamente no sucesso deste tipo de produto, bem como na percepção do consumidor (BERČÍK et al 2016;BONNY et al 2017;LEE;YUN, 2015;MONTELEONE et al 2017).…”

Section: Introductionunclassified

Análise sensorial dinâmica de bacon defumado com madeiras de reflorestamento: um estudo com consumidores

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Análise sensorial clássica 2. Métodos rápidos de análise 3. Produto cárneo defumado 4. Dominância temporal de sensações I. Título Agradecimentos À Deus criador de todas as coisas, senhor de minha vida, meu mestre, guia, luz, segurança para meus passos. À Virgem Santissima minha senhora e mãe, por nesses dois anos ter me educado, formado, ensinado, protegido e guiado, por não me deixar fraquejar e estar sempre ao meu lado. Aos meus pais, Geli e Neuzi, pelo dom da vida, pela graça de poder ter uma família aqui na terra, por todo apoio incondicional, amor, cuidado, paciência, carinho e dedicação ao longo de toda a minha caminhada e em especial nesses últimos dois anos, e também por todo o suporte financeiro. Ao Gabriel, meu amor, meu parceiro, companheiro, por toda paciência, carinho, cuidado, por me apoiar, me encorajar e me dar forças. Ao meu irmão Felipe, à Taissa, e meus sobrinhos Pedro e Miguel, porque por muitas vezes vocês foram a minha esperança, meu consolo, minha força para lutar, obrigada por me incentivarem a amar. Á professora Carmen, por sua orientação dedicação, paciência, correções e conselhos nesses dois anos. Ao Erick, pela amizade sincera, paciência, pela força, incentivo emocional e intelectual. Pelo apoio no desenvolvimento do projeto, na escrita, nas correções, enfim, obrigada por tudo amigo, minha dívida será eterna! Á diocese de Piracicaba, ao MUR Piracicaba, aos GOU's e GPP, na pessoa dos servos, Fábio, Isabel, Selma, Donizete, Rosinha, Marco, Leonora. Vocês foram minha família, meu consolo e esperança nesse tempo. Rosto de Cristo em minha vida, que Deus abençoe a vida de cada um de vocês, gratidão a todos. Á Bruna princesa, por tudo.

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The impact of parameters of store illumination on food shopper response

Cited by 37 publications

References 46 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Deep Learning for EEG-Based Preference Classification in Neuromarketing

Análise sensorial dinâmica de bacon defumado com madeiras de reflorestamento: um estudo com consumidores

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