In the market for functional products, identifying the tastes and preferences of consumers is crucial to defining marketing strategies due to its difficult to penetrate nature. This article presents a study using multivariate data by means of Principal Component Analysis (PCA) and Electroencephalogram (EEG) to detect consumers' neuronal response to functional products and predict their purchasing decisions. The usefulness of neuromarketing was examined to evaluate the preferences of 16 subjects aged 20 to 29 years, who evaluated a series of samples through the sense of taste. Due to PCA being associated with frontopolar 1 located in the prefrontal cortex, we used PCA to reduce the dimensionality of the data obtained from EEG, finding that the low beta and low gamma frequency bands, along with the percentages of attention and meditation of the panelists, are the main factors in decision making. In addition, we used some digital image processing tools to support the evidence that there is a difference in the brain activity of the panelists when they taste functional products that they like and dislike. This finding can improve our understanding of decision-making and can be used in the food sector to generate a commercial strategy.

Since many children with Down syndrome have difficulties with emotion recognition, there is a significant application gap in assistive technologies and affective computing that could be addressed. Conventional deep learning methods, which depend on the standard raster-scan flattening operation, achieve limited accuracy in this population because they fail to preserve spatial locality. In this paper, we propose a novel Hilbert space-filling curve optimization for neural network flattening layers, specifically designed not only to address these gaps in assistive technologies for this vulnerable group who are currently underserved by affective computing, but also to provide a framework for researchers seeking to fine-tune the architecture of artificial neural networks. Our approach retains spatial coherence using Hilbert indexing, implemented as flexible Keraslayers that are compatible with standard architectures such as VGG16 and ResNet50. A comprehensive analysis across multiple datasets reveals a 4% improvement in emotion recognition accuracy compared to Hilbert. The Hilbert optimization achieves 71% precision in Down syndrome emotion classification while reducing processing overhead by approximately 5%. By closing the emotion recognition gap with spatial-aware deep learning, our work contributes to more equitable AI for healthcare and advances the development of assistive technologies for neurodiverse populations, with near-term clinical utility in pediatrics and broader applications in affective computing.

This study aims to investigate the relationship between consumer neuroscience and neuromarketing using a multivariate methodology. Tools such as Principal Component Analysis (PCA) and deep learning neural networks were employed to interpret consumer responses to functional products. To this end, EEG signals were collected, recorded, and analyzed from 83 participants aged 20 to 29 to identify significant neural markers related to food consumption decisions. Key factors influencing decision making were identified, including low beta and gamma frequency bands. Participants’ levels of attention and reflection also played a role. The findings validate the effectiveness of the proposed method, demonstrating its applicability in various fields requiring accurate and reliable classification. Furthermore, some possible applications of this topic are mentioned in the food industry section, with the aim of enabling them to develop personalized nutritional strategies based on the results obtained from the brain activity of consumers.