Decoding Taste from EEG: Gustatory Evoked Potentials During Wine Tasting

Decoding taste from brain activity is of paramount importance for understanding brain responses to gustatory stimuli with applications in the food industry, neuro-marketing, objective evaluation of consumer's preferences, and addiction prevention/treatment. However, progress in this field has been thwarted by challenging data acquisition and the multimodal aspects of gustatory events, which make traditional decoding based on event-related potentials (ERP) very difficult. Additionally, emerging approaches such as Deep Learning come short to give insights into decoding taste because their need of big amounts of data. In this paper, we propose a processing and analytical pipeline based on Multivariate Pattern Analysis (MPA) and Global Field Power (GFP) to analyze gustatory evoked potentials recorded with high-density scalp electroen-cephalography (EEG). Based on this analysis we show the temporal evolution of neural codes for identification of water-vs-wine and wine-vs-wine dyads.