RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing

Abstract

Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. 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’ 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 (“buy”/ “not buy”), 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 two neuromarketing related datasets of different nature. The first is employed to showcase the efficacy of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field.

Bibliometric Analysis of Neuromarketing and Consumer Behaviour: A Comprehensive Review From 2000 To 2023

“…Thus, Costa-Feito et al ( 2023) describe it as an ideal tool to assess consumers' immediate reactions to advertisements, product presentations, and purchasing decisions. As a result of EEG, consumer choices can be explained in terms of attention, perception, and emotional engagement (Georgiadis et al, 2022). Research laboratories, simulated shopping environments, and even real retail spaces can be used to conduct EEG studies.…”

Using linear parameter varying autoregressive models to measure cross frequency couplings in EEG signals

“…In non-invasive brain computer interfaces (BCI), the concept of CFC has not yet been fully explored. To date, only a few studies have investigated the prospect of CFC in BCI systems (Gysels and Celka, 2004;Wei et al, 2007;Dimitriadis and Marimpis, 2018;Georgiadis et al, 2019;Feng et al, 2020;Gwon and Ahn, 2021). Gysels and Celka (2004) and Wei et al (2007) used CFC as a measure of functional connectivity between different brain regions and demonstrated its potentiality as a feature for mental task classification.…”

“…By employing CFC as a feature for decoding flashing images and stimulus presentations, high classification accuracy and information transfer rates were achieved. Similarly, in Georgiadis et al (2019), Feng et al (2020), and Gwon and Ahn (2021) CFC was introduced as the main building block of motor imagery based decoding schemes.…”