Inspiration

In the last few, high quality research grade EEG headsets have become available at an affordable price point for consumers. At the same time, open source big data tools have become ubiquitous and cloud computing has become a commodity.

The confluence of these trends leads our team to ask, can we find new knowledge and patterns in brainwaves across large amounts of people, as opposed to a few subjects in the neuroscience lab?

What it does

Our application tracks both the applications you are currently using and your current brain state. By correlating the two, it reports down to earth advice in a conversational style to help the user learn about their habits. For example, if we find high levels of alpha waves while the user is browsing Youtube, we may suggest that they browse Youtube to relax when they're feeling stressed.

How we built it

The system is primarily built around the Cloudbrain API (http://getcloudbrain.com), which allows the streaming of Muse brainwave data and the tagging of correlate events. We modified ulogme, an open source application tracking software, to stream application data (Facebook, Netflix, TechCrunch) to Cloudbrain. Finally, we built a web application using AngularJS to show the user information cards and conversational messages.

Challenges we ran into

For the most part, our team focused on execution. We are all experienced in the technologies we used, but trying to complete a project of this scope required a lot of team discipline. We also find that deriving inferences from brainwave data is really hard. Trying to create advice on that data is even harder. With more data collection, we hope this will become easier.

Accomplishments that we're proud of

The scope of the project is really large. We're mostly proud that we were able to build a functioning application with only a team of three. We're excited that we can share this idea that large amounts of data lead to new scientific research.

What we learned

From a technical perspective, we learned ES6, the new standard for JavaScript. We learned that a small team can sometimes be faster, more focused and more productive than a large team.

What's next for BrainPal

Since the system is functional now, the next steps are to ensure its robustness with a test-suite, add authentication, and roll it out to users.

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