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Neuromarketing with EEG

Neuromarketing, or consumer neuroscience, applies brain-imaging and biometric measurement to study how consumers respond to advertising, products, brands, and prices. Electroencephalography (EEG) is its most widely used tool because it records electrical activity from scalp electrodes with millisecond resolution, capturing the rapid dynamics of attention and emotion as a stimulus unfolds. From the cleaned signal, researchers derive indices such as frontal alpha asymmetry, which Richard Davidson's work links to approach versus withdrawal motivation, along with engagement ratios from beta, alpha, and theta power and event-related potentials time-locked to stimulus events. These neural measures are often combined with autonomic biometrics such as galvanic skin response and heart rate, and with fMRI in lab settings, to triangulate emotional arousal and valence. Plassmann, Venkatraman, Huettel, and Yoon's 2015 Journal of Marketing Research article set out the legitimate applications and the methodological challenges of this field. The promise is to capture moment-to-moment, non-conscious responses that consumers cannot or will not verbalize, while the discipline insists those signals be interpreted cautiously and validated against behavior.

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出典

  1. Plassmann, H., Venkatraman, V., Huettel, S., & Yoon, C. (2015). Consumer Neuroscience: Applications, Challenges, and Possible Solutions. Journal of Marketing Research, 52(4), 427-435. DOI: 10.1509/jmr.14.0048
  2. Davidson, R. J. (2004). What does the prefrontal cortex 'do' in affect: perspectives on frontal EEG asymmetry research. Biological Psychology, 67(1-2), 219-233. DOI: 10.1016/j.biopsycho.2004.03.008

このページの引用方法

ScholarGate. (2026, June 23). Neuromarketing with EEG and Biometrics (Consumer Neuroscience). ScholarGate. https://scholargate.app/ja/marketing/neuromarketing-eeg

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ScholarGateNeuromarketing with EEG (Neuromarketing with EEG and Biometrics (Consumer Neuroscience)). 2026-06-24に以下より取得 https://scholargate.app/ja/marketing/neuromarketing-eeg · データセット: https://doi.org/10.5281/zenodo.20539026