Discriminating different color from EEG signals using Interval-Type 2 fuzzy space classifier (a neuro-marketing study on the effect of color to cognitive state)

Rakshit, Arnab; Lahiri, Rimita

doi:10.1109/icpeices.2016.7853388

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…Rakshit and Lahiri used SVM and interval-type 2 fuzzy classifiers to classify red blue and green colors from EEG signals. Their model achieved the classification with 78.81% average accuracy for SVM-based color classification [67]. However, IT2FS achieved the highest 80.04% mean accuracy compared to other classifiers in the experiment.…”

Section: Machine Learning Application In Neuromarketingmentioning

confidence: 92%

“…In 2016, Fan and Touyama applied spatial and temporal principal component analysis (STPCA) for feature extraction from ERP P300 signal. Rakshit and Lahiri [67] used a different approach to extract features from EEG signals. They used Welch method for one-sided power spectral density estimate and then applied a 256-point DFT algorithm on hamming window of length 50 to extract features.…”

Section: Brain Signal Processing In Neuromarketingmentioning

confidence: 99%

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Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

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Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

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Section: Machine Learning Application In Neuromarketingmentioning

confidence: 92%

Section: Brain Signal Processing In Neuromarketingmentioning

confidence: 99%

Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

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“…2011; Ohme and Matukin, 2012;Di Flumeri et al, 2016;Rakshit and Lahiri, 2016;Dulabh et al, 2018). EEG carries a relatively low cost for the equipment and tests.…”

Section: Electroencephalogrammentioning

confidence: 99%

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

et al. 2020

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The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

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“…A cor vermelha foi a que obteve melhor diferenciação pelo classificador utilizado, indicando que a mesma conseguiu se destacar entre as outras e provocou uma atividade cerebral peculiar, seguida da cor azul, amarela e verde. Em [15] foram obtidos resultados similares aos deste trabalho, onde a cor identificada com maior acurácia foi a vermelha, seguida pela azul, verde e amarela.…”

Section: Resultsunclassified

“…Segundo [16], os seres humanos são mais sensíveis a cores com maior comprimento de onda, como a cor vermelha (na faixa de 625nm a 740nm). Os autores de [15] afirmam que diferentes cores estão associadas a diferentes emoções e estados mentais; e a cor vermelha provocaria também maior ativação do estado mental. Entretanto, segundo o experimento realizado por [8], a cor vermelha é relatada como a menos confortável para os usuários.…”

Section: Resultsunclassified

Influência Do Padrão De Cor Em Potenciais Evocados Visuais

Borges¹,

Zambalde²,

Soares³

2018

Anais Do v Congresso Brasileiro De Eletromiografia E Cinesiologia E X Simpósio De Engenharia Biomédica

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Resumo: Interfaces cérebro-máquina têm se apresentado como uma grande promessa para a atualidade. A interação entre cérebro e computadores pode ser realizada de diversas maneiras, uma delas é por potencial evocado visual por meio de cores. Este trabalho visa identificar a influência de cores nos sinais eletroencefalográficos para serem utilizadas como meio de ativação de interfaces. Foram realizados testes envolvendo voluntários hígidos onde foram analisadas as influências de 4 cores (amarelo, azul, verde e vermelho). Foi realizada análise estatística para comparações entre as potências do sinal nos períodos com e sem estimulação visual para constatar quais canais apresentavam diferença significativa. Os canais identificados foram O1, O2, P3, P4 e PZ. Por fim, um classificador multiclasse (SVM-ECOC) foi utilizado para realizar a diferenciação entre as cores. Como resultado, a cor vermelha foi a melhor identificada pelo classificador, enquanto a verde obteve a menor acurácia. Palavras-chave:Eletroencefalografia, potencial evocado, cores, classificador SVM. [2]. A intensidade dos sinais captados na superfície do escalpo varia de 0 a 200µV, com frequências mais proeminentes entre 0 e 50 Hz. Alguns padrões de onda distintos foram identificados na atividade EEG, como, por exemplo, ondas delta (frequências menores que 3,5Hz), theta (4 a 7Hz), alfa (8 a 13Hz) e beta (14 a 30Hz) [3]. AbstractA resposta cerebral gerada com um estímulo, ou seja, o potencial elétrico produzido no córtex em resposta a um estímulo é conhecida como potencial evocado. Os estímulos podem ser: visuais, [1], auditivos [4] ou somato-sensoriais [5]. Todas estas modalidades têm sido objeto de investigação em literatura recente, no intuito de desenvolver mecanismos de comunicação baseados na atividade cortical (interfaces cérebro-máquina -ICMs) para sujeitos com deficiências motoras diversas.Alguns estudos recentes [6,7,8,9, 10] avaliaram diferentes estímulos visuais com o objetivo de desenvolver ICMs confiáveis e confortáveis. No entanto, diversos aspectos ainda necessitam ser aprofundados para que tal técnica possa ser efetivamente utilizada no futuro. Um dos aspectos ainda sob investigação, é o efeito de diferentes cores na atividade cortical e na performance de ICMs.Neste sentido, este artigo investiga alterações específicas na atividade cerebral provocadas por diferentes estímulos por meio de cores, bem como a possibilidade de se identificar padrões corticais representativos de cada cor e qual delas apresenta maior eficácia para uso em ICMs. Materiais e métodosCinco indivíduos hígidos, com idades entre 21 e 32 anos, foram recrutados para o estudo. Os critérios de

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Discriminating different color from EEG signals using Interval-Type 2 fuzzy space classifier (a neuro-marketing study on the effect of color to cognitive state)

Cited by 16 publications

References 15 publications

Technological advancements and opportunities in Neuromarketing: a systematic review

Technological advancements and opportunities in Neuromarketing: a systematic review

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

Influência Do Padrão De Cor Em Potenciais Evocados Visuais

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