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NeuroBioSense: A multidimensional dataset for neuromarketing analysis.

Büşra Kocaçınar1, Pelin İnan1, Ela Nur Zamur1

  • 1Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey.

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|March 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the NeuroBioSense dataset, capturing physiological and facial responses to advertisements. It aims to advance neuromarketing research by analyzing consumer emotions and decision-making processes.

Keywords:
BiosignalsImage processingNeuromarketing

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Area of Science:

  • Neuromarketing and Consumer Behavior
  • Interdisciplinary studies integrating neuroscience and marketing

Background:

  • Investigating consumer decision-making in marketing and sales is complex.
  • Multicultural influences and global societal conditions add layers to consumer behavior analysis.
  • Neuroscience tools offer new ways to understand cognitive processes in international marketing.

Purpose of the Study:

  • To introduce the NeuroBioSense dataset for analyzing consumer responses.
  • To enable classification of consumer emotions and reactions to branding advertisements.
  • To facilitate research on the relationship between consumers, advertising, and neuromarketing.

Main Methods:

  • Collected physiological signals (photoplethysmography, electrodermal activity, body temperature) using Empatica e4 wearable sensors.
  • Recorded facial images of 58 participants (aged 18-70) watching cosmetic, food, and car advertisements.
  • Utilized a 7-class emotion evaluation scale (Joy, Surprise, anger, disgust, sadness, fear, neutral).

Main Results:

  • The NeuroBioSense dataset comprises physiological data, facial images, and demographics.
  • Data were collected across three distinct advertising phases for 58 participants.
  • The dataset supports analysis of consumer responses across various emotion classes.

Conclusions:

  • The NeuroBioSense dataset is a valuable resource for neuromarketing and consumer behavior research.
  • It will aid in developing emotion classification models and understanding advertising effectiveness.
  • This dataset supports advancements in analyzing consumer-product interactions and decision-making.