Preliminary diagnosis with wearables offers a cheap and easy way to catch possible signs of disease as it's developing. Neural disorders are highly prevalent in all generations, particularly depression and anxiety. The ability to detect such an illness in advance would allow for a wider assortment of treatment options and ultimately offers a higher chance of successful treatment.

What it does

Our app uses a Muse headset to take raw data from EEG scans and neural wave emissions, and compares the results against data from medical studies to provide a likely set of illnesses, such as ADHD. We built a few Android applications designed to stimulate the brain in order to measure response time and memory while gathering data.

How we built it

We have the Muse feeding data into a Python server for processing and analytics, and an Android app to use while working with the Muse to measure response time and memory and report the results over Spark to a "doctor".

Challenges we ran into

It took a lot of fiddling to get data from the Muse, and finding public data to compare our results against was difficult.

Accomplishments that we're proud of

With three first-time hackers, we all learned a lot about servers, data aggregation and analytics, and interfacing hardware.

What we learned

Using REST with the Spark API, building Android applications, and creating small servers to record and analyze data.

What's next for Brainwave

Better EEG hardware, as the Muse is not as accurate as we'd like. Test data to build and train a classification model for a full ML approach to the problem. Obtaining more data about other neural diseases to test for.

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