Advertising Liking Recognition Technique Applied to Neuromarketing by Using Low-Cost EEG Headset

In this paper a new neuroscience technique is applied into Marketing, which is becoming commonly known as the field of Neuromarketing. The aim of this paper is to recognize how brain responds during the visualization of short advertising movies. Using low cost electroencephalography (EEG), brain regions used during the presentation have been studied. We may wonder about how useful it is to use neuroscience knowledge in marketing, what can neuroscience add to marketing, or why use this specific technique. By using discrete techniques over EEG frequency bands of a generated labeled dataset, C4.5 and ANN learning methods have been applied to obtain the score assigned to each ads by the user. This techniques allows to reach more than 82% of accuracy, which is an excellent result taking into account the kind of low-cost EEG sensors used.Ministerio de Economía y Competitividad TIN2013-46801- C4-1-rJunta de Andalucía TIC-805

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research.

INTRODUCTION: The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. METHODS: Search terms 'neuromarketing' and 'consumer neuroscience' identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. RESULTS: Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. CONCLUSIONS AND IMPLICATIONS: FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses