Advancing research on odor-induced sweetness enhancement: A EEG local-global fusion transformer network for sweetness quantification combined with EEG technology

Abstract

Reducing sugar intake is crucial for health, and odor sweetening enhances food enjoyment and quality perception. Current research relies on subjective manual sensory evaluations, which are poorly reproducible. Traditional methods also fail to capture dynamic neural responses to odor-induced sweetness. We propose an electroencephalogram local-global fusion transformer network (EEG-LGFNet) model to decode this impact objectively. Electroencephalogram data were collected from 16 subjects under different odor and sucrose stimuli. The model captures complex neural signals by integrating local and global feature extraction mechanisms. Its performance was validated across three-time windows, demonstrating efficacy over various temporal ranges. Analysis of the coefficient of determination across brain regions confirmed the importance of the frontal, central, and parietal areas of sweetness perception. The EEG-LGFNet model excelled in quantifying odor-enhanced sweetness, significantly outperforming state-of-the-art models. This research offers new insights into odor sweetening, with applications in food development, personalized nutrition, and neuroscience.