EEG-Informed Deep Neuro-Recommendation of Video Advertisements via Correlative Capsule Split Attention and Forward Harmonic Learning

DOI

10.2991/978-94-6239-723-1_17How to use a DOI?

Keywords

Component Artificial Intelligence (AI); Brain-Computer Interface (BCI); Emotive Insight 5; Neural Responses; Brainwave Frequencies; Machine Learning Algorithms

Abstract

This study seeks to examine a new advertisement recommendation system that is brainwave powered. The architecture is based on the Emotive Insight technology, Brain-Computer Interface (BCI) and Artificial Intelligence (AI) elements. The main goal is to improve marketing prescriptions based on neural activities of users. In order to achieve this objective, Emotive Insight devices record alpha, beta, theta, delta and gamma waves in real time. The data was also stored in the form of a.csv and a.edf file to facilitate detailed analysis. During the experiment the subjects are shown three different adverts namely Snickers, Dairy Milk and 5-star advertisements as their brain activity is constantly tracked. Subjects are exposed and give feedback on their preferences in advertisements which is compared with brainwaves of these preferences. Analysis stage entails massive pre-processing, elimination of artifacts and feature extraction to come up with significant patterns. Finally, investigators create a predictive framework based on a combination of machine learning algorithms and evaluate the effectiveness of the framework on the criterion of performance measures, such as accuracy, precision, recall, and F1 score.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nileema Prasad Gaikwad
AU  - Jagannath Nalavade
PY  - 2026
DA  - 2026/07/14
TI  - EEG-Informed Deep Neuro-Recommendation of Video Advertisements via Correlative Capsule Split Attention and Forward Harmonic Learning
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 186
EP  - 196
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_17
DO  - 10.2991/978-94-6239-723-1_17
ID  - Gaikwad2026
ER  -