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 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’s superiority against popular alternatives in the field.

Revisiting Riemannian geometry-based EEG decoding through approximate joint diagonalization

“…Indeed, Riemannian geometry has been proved to be an invaluable asset in EEG signal analysis (e.g. [46]), however it requires rigorous mathematical procedures that may hinder its wider adoption. Moreover, this problem becomes more acute given that classical pattern recognition and machine learning schemes cannot be readily employed in Riemannian schemes.…”

Emotions in fear communication: A cross‐cultural neuromarketing approach

“…Finally, memorization measures the intensity of cognitive processes related to forming future memories while presenting a stimulus. It captures the degree of storage, encoding, and retention in memory (Vecchiato et al, 2011) and the degree of recall that may influence future decision processes (Georgiadis et al, 2022). We express memorization in percentages.…”

Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception

“…Chen et al (2015) published a paradigmatic study combining fMRI and MVPA-based (multi-voxel pattern analysis) data analysis (Mumford et al, 2012) to study brand associations in the consumers' brain under the brand personality framework (Aaker, 1997). Other recent studies have been combining machine learning data analysis methods with neuroscientific or biometric acquisition methods, such as facial coding (Filipović et al, 2020), electroencephalography (EEG) for preference detection (Aldayel et al, 2021), in this case, comparing distinct methods for feature extraction and classification, labeling with "buy" and "not buy" and testing with an ensemble classifier over EEG data (Georgiadis et al, 2022), or using support vector machine (SVM) over t-statistics fMRI images to study the more effective way to present apparel goods in an online shop (Jai et al, 2021). The approach in the present study is to re-analyze fMRI data searching for functional brain neural networks-because brain functions emerge from networks (Yuste, 2015)-that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands.…”