How to Measure Cerebral Correlates of Emotions in Marketing Relevant Tasks

Giovanni Vecchiato, Patrizia Cherubino, Anton Giulio Maglione, Maria Trinidad Herrera Ezquierro, Franco Marinozzi, Fabiano Bini, Arianna Trettel, Fabio Babiloni

Research output: Contribution to journalArticlepeer-review

Abstract

Nowadays, there is a growing interest in measuring emotions through the estimation of cerebral variables. Several techniques and methods are used and debated in neuroscience. In such a context, the present paper provides examples of time-varying variables related to the estimation of emotional valence, arousal and Approach-Withdrawal behavior in marketing relevant contexts. In particular, we recorded electroencephalographic (EEG), galvanic skin response (GSR) and heart rate (HR) in a group of healthy subjects while they are watching different TV commercials. Specifically, results obtained in the Experiment 1 shows a significant increase of cortical power spectral density across left frontal areas in the alpha band and an enhance of cardiac activity during the observation of TV commercials that have been judged pleasant. In the Experiment 2, frontal EEG asymmetry, GSR and HR measurements are used to draw cognitive and emotional indices in order to track the subject’s internal state frame by frame of the commercial. A specific case study shows how the variations of the defined Approach-Withdrawal and emotional indices can distinguish the reactions of younger adults from the older ones during the observation of a funny spot. This technology could be of help for marketers to overcome some of the drawbacks of the standard marketing tools (e.g., interviews, focus groups) usually adopted during the analysis of the emotional perception of advertisements.

Original languageEnglish
Pages (from-to)856-871
Number of pages16
JournalCognitive Computation
Volume6
Issue number4
DOIs
Publication statusPublished - Dec 4 2014

Keywords

  • EEG
  • Emotions
  • Heart rate
  • Neuromarketing
  • TV commercials

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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