# Engagement Estimation in Advertisement Videos with EEG

arXiv: https://arxiv.org/abs/1812.03364 · HTML: https://mlsanity.com/paper/1812.03364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2208.00877/) (2022)

## Abstract (arXiv)

Engagement is a vital metric in the advertising industry and its automatic estimation has huge commercial implications. This work presents a basic and simple framework for engagement estimation using EEG (electroencephalography) data specifically recorded while watching advertisement videos, and is meant to be a first step in a promising line of research. The system combines recent advances in low cost commercial Brain-Computer Interfaces with modeling user engagement in response to advertisement videos. We achieve an F1 score of nearly 0.7 for a binary classification of high and low values of self-reported engagement from multiple users. This study illustrates the possibility of seamless engagement measurement in the wild when interacting with media using a non invasive and readily available commercial EEG device. Performing engagement measurement via implicit tagging in this manner with a direct feedback from physiological signals, thus requiring no additional human effort, demonstrates a novel and potentially commercially relevant application in the area of advertisement video analysis.