2026-07-12T01:30:02Zhttps://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

00925njm 22002777a 4500 dc Romeo-Arroyo, Elena author soria, javier author Mora Gijón, María author Laport Lopez, Francisco author Moreno Fdz. de Leceta, Aitor author VÁZQUEZ-ARAÚJO, LAURA author 2022 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. 1424-8220 https://hdl.handle.net/20.500.11984/14457 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