BRAIN: Behavioral Responses and Artificial Intelligence Neural-Modeling for Consumer Decision-Making
Keywords: Decision-Making; PCA; DCNN; Neuromarketing
TL;DR: This study uses PCA and deep learning to analyze EEG signals, identifying neural indicators linked to consumer preferences, suggesting applications in personalized nutrition based on brain patterns.
Abstract: This research investigates consumer neuroscience and neuromarketing through a multivariate methodology, employing Principal Component Analysis (PCA) and deep learning neural networks to interpret consumer responses to functional products. EEG signals were collected, recorded, and analyzed from 16 individuals aged 20 to 29 to identify significant neuronal markers related to consumer choices. The pivotal factors influencing decision-making were identified as the low beta and low gamma frequency bands, as well as participants' attention and meditation levels. The findings validate the effectiveness of our approach, demonstrating its applicability across various fields requiring accurate and reliable classification. Additionally, it is recommended to explore the potential applications of this study in the food industry by creating personalized nutrition strategies based on individuals' brain activity patterns.
Primary Area: learning on time series and dynamical systems
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 10888
Loading