EEG based detection of area of interest in a web page

Abstract

We focus on the problem of detection of the user's area of interest within a single web page, or the web page of interest within different web pages. Current methods either use some kind of manual ranking, or apply parameters such as the time the user spends on a specific area of the page to determine the area of interest. We postulate that the attention level of the user while browsing is a more reliable indication of the user's level of interest. We use EEG inputs from a NeuroSky MindWave headset to capture the user's attention level in real time. A background script in a web browser in a mobile device captures the part of the webpage currently being browsed by noting the percentage of the page that the user has scrolled to. The attention level and the percentage of the page scrolled are mapped using the timestamp as the key. Our solution is integrated with the mobile web browser architecture. Using our method, we determine and map the average attention level within the same page, and across different pages, for a range of websites and users. This can be useful in a number of applications including: providing inputs of user behavior to web developers for better web design, ranking different websites or videos as per user interest, inserting ads in the regions of a web page where the user is more likely to pay attention to.