Analysis of EEG signals and its application to neuromarketing

Yadava, Mahendra; Kumar, Pradeep; Saini, Rajkumar; Roy, Partha Pratim; Dogra, Debi Prosad

doi:10.1007/s11042-017-4580-6

Cited by 187 publications

(133 citation statements)

References 29 publications

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“…1) Frontal Band Powers (FBP) -Powers for each frequency band (Delta, Theta, Alpha, Beta, and Gamma) were averaged across time per commercial viewing (Boksem & Smidts, 2015;Braeutigam et al, 2004;Khushaba et al, 2012Khushaba et al, , 2013Koelstra et al, 2012;Luck, 2014;Ravaja et al, 2013;M. E. Smith & Gevins, 2004;Telpaz et al, 2015;Yadava et al, 2017). In order to increase our external validity, we focused on the frontal electrodes to resemble various EEG systems with only a few electrodes that are commonly used in the industry.…”

Section: Methodsmentioning

confidence: 99%

“…Despite the fact that EEG is commonly used in the neuromarketing industry (see NMSBA Website), and that there is accumulating data linking various EEG signals with value-based choice (Dmochowski, Sajda, Dias, & Parra, 2012;Fuentemilla et al, 2013;Khushaba et al, 2013;San Martin, Appelbaum, Pearson, Huettel, & Woldorff, 2013;Sutton & Davidson, 2000), only several academic studies attempted to predict subjects' stated preferences or actual choices (Kong, Zhao, Hu, Vecchiato, & Babiloni, 2013;Ravaja, Somervuori, & Salminen, 2013;Telpaz, Webb, & Levy, 2015;Vecchiato et al, 2011;Yadava, Kumar, Saini, Roy, & Prosad Dogra, 2017), or population marketing success (Barnett & Cerf, 2017;Boksem & Smidts, 2015;Dmochowski et al, 2014;Guixeres et al, 2017;Venkatraman et al, 2015). However, importantly, nearly all these previous studies did not examine if their prediction accuracy was above and beyond the prediction accuracy of traditional marketing measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Hence in the current study, we construct a general set of EEG measures that is a combination of measures that were previously shown to have predictive power. Specifically, we combine information on frequency band powers for estimating valuation (Boksem & Smidts, 2015;Braeutigam, Rose, Swithenby, & Ambler, 2004;Khushaba et al, 2012Khushaba et al, , 2013Ravaja et al, 2013;Yadava et al, 2017), hemispheric asymmetries for estimating approach/avoidance tendencies (Davidson, 1998;Laurence & Gerhold, 2016;Ravaja et al, 2013;Vecchiato et al, 2011), and inter-subject correlations (Barnett & Cerf, 2017;Dmochowski et al, 2014;Hasson, Nir, Levy, Fuhrmann, & Malach, 2004) for estimating engagement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

Hakim

klorfeld

Sela

et al. 2018

Preprint

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A basic aim of marketing research is to predict consumers' preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects' individual future preferences over the products and the commercials' population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials' effectiveness using a standard questionnaire commonly used in marketing research.We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials' population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects' preferences and marketing campaigns' success.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

Hakim

klorfeld

Sela

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Examples of such studies are provided in this paragraph to enable the reader to get a sense of the impact of consumer grade devices. Two papers have reviewed past literature on the study of human behavior using brain signals for consumer neuroscience and neuromarketing applications [42], [43]. In addition, a consumer-related research study showcased the effect of color priming on dress shopping, measured by EEG [44].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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“…The EEG helmet is a device that through electrodes records in a non-invasive way 14 channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) of the human brain and through an algorithm, it transforms them into cognitive states in real time (Kim et al, 2018). This is functional to the analysis of the behaviours (Aspinall et al, 2015;Berka et al, 2004, n.d.;Boutani et al, n.d.;Chynal et al, 2016;Gevins et al, n.d.;Hunter et al, n.d.;Itil et al, n.d.;Mavros et al, 2012;Sharma et al, 2017;Yadava et al, 2017). In fact, the raw data collected concerning brain impulses are interpreted by algorithms related to the EEG helmets and transformed into six cognitive states: valence, arousal, stress, meditation, focus, engagement (Badcock et al, 2015(Badcock et al, , 2013Kotowski et al, 2018).…”

Section: Means and Methodsmentioning

confidence: 99%

Survey and Co-Design the Urban Landscape. Innovative Digital Path for Perception Analysis and Data-Driven Project

Bianconi

Filippucci

Seccaroni

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The research presents the participatory processes implemented using Eye-track and EEG to analyse and "survey" the landscape, the immaterial relation that determines the quality of places. If it is always feasible to measure quantities, it is more complex to bring out data and interpretations on the values of places. This path based on the centrality of vision, it focuses on the possibility of detecting through biosensors what attracts the eye most, but also on the effect of vision of this on man. By combining these instruments and their data, it is possible to associate the observer's staring with the sensations he feels, thus explicitly explaining the concept of imageability expressed by Kevin Lynch, it is possible to individuate precisely which elements of the urban space attract the observer and what kind of status arouse in him.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, SpainThis contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W15-165-2019 | © Authors 2019. CC BY 4.0 License.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, Spain This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W15-165-2019 | © Authors 2019. CC BY 4.0 License. Ancona, L., 1970. Dinamica della percezione. Mondadori, Milano.Anderson, D.M., 2002. Build-to-order and mass customization : the ultimate supply chain management and lean manufacturing strategy for low-cost on-demand production without forecasts or inventory.

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Analysis of EEG signals and its application to neuromarketing

Cited by 187 publications

References 29 publications

Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

Consumer Grade Brain Sensing for Emotion Recognition

Survey and Co-Design the Urban Landscape. Innovative Digital Path for Perception Analysis and Data-Driven Project

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