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.

Keywords: BCIs; Covariance Matrices; Electroencephalography; Neuromarketing; Riemannian Geometry.

Fig. 1

Flowchart of the ^RNeuMark methodology

Fig. 2

Experimental protocol for the static dataset. Six different image collections were delivered to the participant, who was allowed to select products from each collection without any restriction

Fig. 3

Brain rhythm-dependent semantic geodesic maps [38] of the single-trial covariance patterns relating to static advertisements in case of subject S5

Fig. 4

The obtained ww-scores for the static dataset. Low ww-score levels indicate high separability between “buy” and “no-buy” brain state

Fig. 5

Classification performance for the decoders of users’ preference in the case of dynamic advertisements