In our proposed solution we track the user’s current attention level using a wearable EEG sensor, and track the currently browsed section of the website. These items are then mapped and fed into a database which resides on the device. Alternatively the database could reside on a centralized cloud server.

There are two ways to achieve this.

One way could be by using JavaScript to write a browser extension using JavaScript to capture the data and map the interest level of the user with the area of the webpage currently being browsed. The advantage of using such a JavaScript based approach is that it can be used with any web browser with little or no modifications needed. However the disadvantage is that it could lead to longer delays.

Another way is to modify the code in the web browser and integrate the tracking of the currently browsed section of the web page and the EEG inputs directly into the browser. The advantage is that it would be much faster. In this paper we went for the approach of integrating our proposed solution into the web browser code.

A. Components of the system

Our setup consists of the following An EEG sensor (such as NeuroSky MindWave) that communicates the data to the mobile device using Bluetooth or similar communication mechanism. A tool (such as EEG Analyzer) to record the attention level in real time along with the timestamp. A database to store the data. This can reside either on the device or on a central cloud server. A background process or script or code integrated in the browser to map the attention level of the user with the part of the website they are currently viewing, using the timestamp as the key. This too can reside either on the device or on a central cloud server.

B. Modified web browser architecture

The modified elements of the web browser code are as follows: Application level changes to insert a content script to perform the tracking A script to receive inputs from the EEG sensor of the current attention level A database on the device to store these mappings

C. Calculating the scroll percentage of the web page

The algorithm used to calculate the scroll percentage has the following steps: The user browses a web page in the web browser An API is called with value arguments i.e. scroll offsets, content dimensions, viewport dimensions, page scale factors etc. These values are used to get the initial and end scrolling percentage of the browsed page.

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