Byrne Adam, Bonfiglio Emma, Rigby Colin, Edelstyn Nicky
Keele Business School, Denise Coates Foundation Building, Keele University, Staffordshire, ST5 5AA, UK.
Faculty of Humanities and Social Sciences, Keele University, Staffordshire, UK.
Brain Inform. 2022 Nov 14;9(1):27. doi: 10.1186/s40708-022-00175-3.
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.
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.
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.
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.
本文讨论了对神经营销中脑电图测量的系统评价结果，确定了哪些脑电图测量是神经营销中消费者偏好的最可靠预测指标。该评价研究了哪种时频（TF）效应（例如，θ波段功率）和事件相关电位（ERP）成分（例如，N400）最能始终如一地反映自我报告的偏好。还研究了机器学习预测，以及脑电图与眼动追踪等生理测量结合使用的情况。
搜索词“神经营销”和“消费者神经科学”确定了使用脑电图测量的论文。如果论文主要用英语以外的语言撰写或未作为期刊文章发表（例如，书籍章节），则将其排除。本评价纳入了174篇论文。
额叶α不对称性（FAA）是偏好最可靠的TF信号，能够区分消费者的积极和消极反应。同样，晚期正电位（LPP）是最可靠的ERP成分，反映了对产品和广告的有意识情感评价。然而，各论文之间的一致性有限，每种测量方法在与偏好和购买行为相关时都呈现出混合结果。
FAA和LPP是对营销刺激、消费者偏好和购买意图的情感反应最一致的指标。通过使用机器学习预测，特别是与眼动追踪或面部表情分析相结合时，FAA和LPP的预测准确性得到了极大提高。
