Applying EEG in consumer neuroscience

Lin, Meng‐Hsien; Cross, Samantha; Jones, William J.; Childers, Terry L.

doi:10.1108/ejm-12-2016-0805

Cited by 121 publications

(136 citation statements)

References 78 publications

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“…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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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…In the last decade it has been flourishing, with more publications appearing each year and a growing community of scientists conducting studies in the field. This has resulted in an abundance of meta‐analyses and reviews (e.g., Ariely & Berns, ; Fortunato, Giraldi, & Oliveira, ; Genevsky & Knutson, ; Harris, Ciorciari, & Gountas, ; Hsu, ; Hsu & Yoon, ; Javor, Koller, Lee, Chamberlain, & Ransmayr, ; Karmarkar & Plassmann, ; Karmarkar & Yoon, ; Lee, Brandes, Chamberlain, & Senior, ; M. H. Lin, Cross, Jones, & Childers, ; Plassmann, Venkatraman, Huettel, & Yoon, ; Schneider & Woolgar, ; Smidts et al, ). In the last 5 years, top business schools (e.g., The Wharton School, Kellogg School of Management, Rotman School of Management, Fox School of Business, MIT Management, RSM at Erasmus, and more) have been hiring an increasing number of neuroscientists and have launched new programs and initiatives in an attempt to apply neuroscience to business applications and theories.…”

Section: Introductionmentioning

confidence: 99%

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing

Hakim

Levy

2018

WIRES Cognitive Science

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In the last decade, the field of consumer neuroscience, or neuromarketing, has been flourishing, with numerous publications, academic programs, initiatives, and companies. The demand for objective neural measures to quantify consumers' preferences and predict responses to marketing campaigns is ever on the rise, particularly due to the limitations of traditional marketing techniques, such as questionnaires, focus groups, and interviews. However, research has yet to converge on a unified methodology or conclusive results that can be applied in the industry. In this review, we present the potential of electroencephalography (EEG)‐based preference prediction. We summarize previous EEG research and propose features which have shown promise in capturing the consumers' evaluation process, including components acquired from an event‐related potential design, inter‐subject correlations, hemispheric asymmetry, and various spectral band powers. Next, we review the latest findings on attempts to predict preferences based on various features of the EEG signal. Finally, we conclude with several recommended guidelines for prediction. Chiefly, we stress the need to demonstrate that neural measures contribute to preference prediction beyond what traditional measures already provide. Second, prediction studies in neuromarketing should adopt the standard practices and methodology used in data science and prediction modeling that is common in other fields such as computer science and engineering. This article is categorized under: Economics > Interactive Decision‐Making Economics > Individual Decision‐Making Psychology > Prediction Neuroscience > Cognition

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“…• Heart rate: Useful as a real-time measure of arousal, attention, cognitive effort, or physical effort (Poels & Dewitte, 2006;Sung, Hartley, Vanman, & Phau, 2016). • Electroencephalography: Useful in measuring various cognitive processes that can be informative about human interactions with the environment, including attention, mismatch detection, valence determination, visual memory, wayfinding, and decision-making, among others (Davidson, 2004;Garrido, Kilner, Stephan, & Friston, 2009;Lin, Cross, Jones, & Childers, 2018;Sternberg, 2010;Weiss & Rappelsberger, 2000). • Electromyography: Useful for capturing facial muscle activity, which relates to evaluating emotional affect (Lajante, Droulers, & Amarantini, 2017;Poels & Dewitte, 2006;Schwarz, 2004).…”

Section: Measurement Techniquesmentioning

confidence: 99%

Virtual Environments for Design Research: Lessons Learned from use of Fully Immersive Virtual Reality in Interior Design Research

Kalantari

Neo

2020

Journal of Interior Design

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What are the current challenges, effectiveness, and future research opportunities for using fully immersive virtual environments (FIVEs) for design research? To answer these questions, we conducted a systematic review to assess peer‐reviewed experimental studies, proof‐of‐concept studies, and conference proceedings that examined the use of FIVEs in the context of interior design research, and 22 studies met our inclusion criteria. Current challenges include lack of perceived realism and immersion, lack of sensory feedback, and time constraints. Key considerations for the effective use of FIVEs include technological considerations, experimental design, measurement techniques, and evaluation methods. Key future research directions include enhancing the realism and immersiveness of FIVEs and promoting multiuser studies.

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Applying EEG in consumer neuroscience

Cited by 121 publications

References 78 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing

Virtual Environments for Design Research: Lessons Learned from use of Fully Immersive Virtual Reality in Interior Design Research

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