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.
Currently the web developer has no way of knowing which section, area or kind of content of the web page is interesting to the user, or which web pages browsed by one or more users are better at sustaining the user’s interest.
Having this information, as to which sections of the page are preferable to most web users, can have several advantages: it can help the website developer design the website in a better way to sustain the user interest for longer, also it can help them to garner more ad revenue by inserting ads near those portions of the website where the user has more interest. It can also help analytics firms like Alexa or Google to rank or analyze websites or media based on the comparative rankings of user interest.
Some studies have been done which track the user’s scroll position and eye gaze [10-11] to determine the time spent on different sections of the page to gauge the user’s interest. However the time spent alone is not that reliable an indicator of actual interest since the user might be distracted while thinking of something else or not understand some section of the website due to which the time spent is longer. Some measure of the actual concentration level of the user while browsing the web page would be more suitable for this purpose.
Other measures to rank webpages or content of webpages (such as YouTube videos or news articles) according to the user’s interest include having a manual ranking for the media content or the web page content. But here too, the manual ranking of the webpage content is prone to manipulation and there is no direct link between the user’s actual level of interest and the content.
Commercial grade EEG sensors such as those developed by NeuroSky  and Emotiv  are becoming popular and used for a number of gaming and focus based applications. Such kits can relay the user’s current attention or concentration level and communicate this data via Bluetooth in real time with a smart device such as a smartphone. Using such kits, one can determine the concentration level of the user when they are browsing a specific section of the web page.
In this paper we use EEG sensors to determine the interest level of the user and map it to the section that the user is reading on the current webpage, captured via the scroll position. Our implementation is integrated with the mobile web browser architecture; although an equivalent method can be used with desktop browsers also.
The rest of the paper is organized as follows: in section 2 we look at related work in the area. Section 3 introduces our proposed solution using EEG to capture the current attention level. Section 4 gives the browser architecture, including the elements to capture the currently browsed section of the webpage and map it to the current attention level captured by the EEG sensor. Section 5 covers the results of some experiments we performed to measure the user’s attention level for different sections of the same web page and with different web pages. Section 6 concludes the paper and suggests some avenues for future work in this area.