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No.: KTP 12421; KB ref. no.: R3200 C659"],"award-info":[{"award-number":["KTP Prog. No.: KTP 12421; KB ref. no.: R3200 C659"]}]},{"name":"Knowledge Transfer Partnership","award":["KTP Prog. No.: KTP 12421; KB ref. no.: R3200 C659"],"award-info":[{"award-number":["KTP Prog. No.: KTP 12421; KB ref. no.: R3200 C659"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2022,12]]},"abstract":"Abstract<\/jats:title>\n Introduction<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n Methods<\/jats:title>\n Search terms \u2018neuromarketing\u2019 and \u2018consumer neuroscience\u2019 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.<\/jats:p>\n <\/jats:sec>\n Results<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n Conclusions and implications<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.1186\/s40708-022-00175-3","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T17:02:52Z","timestamp":1668445372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0761-3929","authenticated-orcid":false,"given":"Adam","family":"Byrne","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma","family":"Bonfiglio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin","family":"Rigby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicky","family":"Edelstyn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"175_CR1","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3389\/fncom.2016.00074","volume":"10","author":"CG Adri\u00e1n","year":"2016","unstructured":"Adri\u00e1n CG, Fuentes-Hurtado F, Valery NO, Provinciale JG, Aus\u00edn JM, Mariano AR (2016) A comparison of physiological signal analysis techniques and classifiers for automatic emotional evaluation of audiovisual contents. 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