In this paper we present the design of a web browser that is responsive to the user’s interest level. The user’s interest level is measured via a wearable Neurosky EEG sensor that gives a value of the user’s attention level in real time. The current scroll position is used to identify which part of the webpage the user is reading currently. Combining the scroll position and the user’s real time attention level, we identify which parts of the webpage, or which website in a list of currently open websites, the user is most interested in. We then make the web browser responsive to the attention level by adding the current webpage to the favorites if the attention level exceeds a threshold, removing a current webpage from the favorites if the attention level goes below a second threshold, generate on-the-fly an automatic index of the portions of the webpage that the user is more interested in from among the currently open tabs and so on. Having a responsive web browser that responds to the user’s level of interest or disinterest can increase the user engagement or satisfaction while browsing. It can also be used to display targeted ads within the interesting portions of the webpage or design better web pages by giving the user interest feedback to the web developer. We describe the methodology and web browser architecture, some tests of the approach on different websites and user interfaces for making the web browser more responsive.
There has been recent interest in determining the interest of the user while reading a webpage or interacting with computing devices [1-2]. This information can be useful for marketers interested in knowing where to place advertisements within a webpage, as well as web developers interested in increasing user engagement with their website. Such work has involved using fMRI and EEG sensors to note the brain activity when the user is interacting with the device. Web browsers currently are not responsive to the user’s level of interest when reading a webpage. The webpage is a one way system where the reader is the passive consumer of the content provided by the web developer. It would be useful to have a system where the browser can gauge the user interest in real time and respond to it, such as by modifying the rendering of the web page or by automatically saving the favorite sections or web pages based on the user’s interest level. Currently without using EEG or such sensors, 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.
Fortunately, in recent times, commercial grade EEG sensors such as those developed by NeuroSky  and Emotiv  are available that are cheap, relatively accurate and have ready API support. The wearable EEG 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 or interest level of the user when they are reading a specific section of the web page. Using this, one can make the web browser responsive to the user’s interest level in real time. In this paper we present the design of a web browser that is responsive to the user’s interest level, using wearable EEG sensors to determine the attention 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 mainly 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 proposes some user interfaces to make the web browser more responsive. Section 6 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 7 concludes the paper and suggests some avenues for future work in this area.