2026-07-11T03:14:08Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/144572026-05-28T06:15:54Zcom_20.500.11984_473com_20.500.11984_14090col_20.500.11984_477

Romeo-Arroyo, Elena soria, javier Mora Gijón, María Laport Lopez, Francisco Moreno Fdz. de Leceta, Aitor VÁZQUEZ-ARAÚJO, LAURA 2026-05-27T10:03:42Z 2026-05-27T10:03:42Z 2022 1424-8220 https://hdl.handle.net/20.500.11984/14457 Using implicit responses to determine consumers’ response to different stimuli is becoming a popular approach, but research is still needed to understand the outputs of the different technologies used to collect data. During the present research, electroencephalography (EEG) responses and self-reported liking and emotions were collected on different stimuli (odor, taste, flavor samples) to better understand sweetness perception. Artificial intelligence analytics were used to classify the implicit responses, identifying decision trees to discriminate the stimuli by activated sensory system (odor/taste/flavor) and by nature of the stimuli (‘sweet’ vs. ‘non-sweet’ odors; ‘sweet-taste’, ‘sweet-flavor’, and ‘non-sweet flavor’; and ‘sweet stimuli’ vs. ‘non-sweet stimuli’). Significant differences were found among self-reported-liking of the stimuli and the emotions elicited by the stimuli, but no clear relationship was identified between explicit and implicit data. The present research sums interesting data for the EEG-linked research as well as for EEG data analysis, although much is still unknown about how to properly exploit implicit measurement technologies and their data. eng Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ © 2022 by the authors electroencephalogram (EEG) sucrose clustering algorithms sensory modality discrimination Exploratory Research on Sweetness Perception: Decision Trees to Study Electroencephalographic Data and Its Relationship with the Explicit Response to Sweet Odor, Taste, and Flavor