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Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers\u2019 choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (\u201cbuy\u201d\/ \u201cnot buy\u201d), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder\u2019s superiority against popular alternatives in the field.<\/jats:p>","DOI":"10.1186\/s40708-022-00171-7","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T10:02:48Z","timestamp":1663322568000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing"],"prefix":"10.1186","volume":"9","author":[{"given":"Kostas","family":"Georgiadis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fotis P.","family":"Kalaganis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vangelis P.","family":"Oikonomou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spiros","family":"Nikolopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikos A.","family":"Laskaris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"171_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-77829-5","volume-title":"Neuromarketing: Exploring the brain of the consumer","author":"L Zurawicki","year":"2010","unstructured":"Zurawicki L (2010) Neuromarketing: Exploring the brain of the consumer. 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