Engagement Estimation in Advertisement Videos with EEG

TL;DR

This paper demonstrates a simple EEG-based framework for estimating viewer engagement with advertisement videos, achieving promising classification results and highlighting potential for commercial applications using accessible EEG technology.

Contribution

It introduces a novel, low-cost EEG approach for implicit engagement measurement in advertisement videos, pioneering a practical method for real-world media analysis.

Findings

F1 score of nearly 0.7 for binary engagement classification

Feasibility of non-invasive EEG for engagement detection in real-world settings

Potential for commercial deployment in advertisement analysis

Abstract

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.