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. 2023 Aug 3;10(1):508.
doi: 10.1038/s41597-023-02392-9.

NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour

Kostas Georgiadis #  1 , Fotis P Kalaganis #  2 , Kyriakos Riskos  3   4 , Eleftheria Matta  5 , Vangelis P Oikonomou  2 , Ioanna Yfantidou  3 , Dimitris Chantziaras  6 , Kyriakos Pantouvakis  5 , Spiros Nikolopoulos  2 , Nikos A Laskaris  7 , Ioannis Kompatsiaris  2
Affiliations

NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour

Kostas Georgiadis et al. Sci Data. .

Abstract

Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers' behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
(a) The NeuMa-dataset experimental protocol/timeline. Six brochure pages including supermarket products were provided to the participants, that had to select products without any restriction. (b) The simultaneously recorded EEG activity and the corresponding mouse clicks embedded in the timeline of EEG traces.
Fig. 2
Fig. 2
Participants’ distribution with respect to particular demographics (age and education level).
Fig. 3
Fig. 3
Participants’ distribution regarding profiling attributes. The low and high levels correspond to 25% and 75% of the measuring scale of the presented attributes accordingly.
Fig. 4
Fig. 4
Illustration of the NeuMa Preprocessed Dataset format.
Fig. 5
Fig. 5
Percentage of EEG recording characterized by excellent (a) and good quality (b), based on the EEG Quality Indices.
Fig. 6
Fig. 6
EEG recordings and the corresponding power spectrums regarding subject S14 for the raw (upper panel) and the pre-processed (lower panel) versions of the dataset.
See this image and copyright information in PMC

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