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This is copied over directly from the devpost submission. If you have any questions just get in touch with one of us.

If you wanna see it in action, check out our YouTube video.


In modern neuroscience focus has achieved great success in extracting thoughts and emotions from EEG data. The muse headband provides a revolutionary possibility to use such data without complex measurement equipment. In our project we present an approach to revolutionize the advertising and streaming industry by evaluating video clips directly by a users' thoughts.

What it does

While watching youtube videos we track a concentration score of the muse headband to measure the attention and excitement of a user. After the video has finished different result plots allow detailed analysis.

How we built it

The application is programmed as a android app. The headband is connected to the mobile device via bluetooth.

Challenges we ran into

The latest version of the official muse api has removed the concentration score, we mainly base our analysis on. Therefore we had to migrate to an older version from 2015.

Accomplishments that we're proud of

Fully functional app to watch youtube videos and view the measured concentration score afterwards. Furthermore the calibration visually in advance.

What we learned

We leaned how do work with EEG data and figured out, how challenging it is to extract useful information.

What's next for aMUSE-measure

Improve precision and incorporate alpha, beta and theta waves explicitly to the attention score. Apply to existing series and confirm correlation.

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