Picture this. You’re jamming out to your favourite song, its hitting all the right notes in all the right places, but as with all things, it comes to an end. The next song then goes on to be totally different, and then kills the mood. We’d like to change that with the muse. Our app is called: Muse-ic.

Museic is an app that allows you to store your brainwave data as you listen to songs you enjoy, give them a rating and a tag for how they made you feel, and then use that data to suggest songs that others have had a similar brainwave experience with.

All of this is made possible with an API muse provides called libmuse. By using the data sent from the muse, we can measure your musical experience through 5 different brainwaves: Alpha Waves, Beta Waves, Gamma Waves, Delta Waves and Theta Waves. Briefly describing each of these waves, alpha waves measure how relaxed/calm you are, delta waves measure how sleepy you are, beta waves measure how much you are actively thinking or problem solving, theta waves measure very deep relaxation/visualization, and gamma waves measure high mental activity and consolidation of information. When you listen to music, these waves will change accordingly with how you experience the music.

Let’s take you through the flow of the program. In our demo, you’d start off at the main screen with a Record Data button and a Suggest Song button. Before you start getting suggestions, we must store some brain data in the program. You’ll press Record Data, then you’ll pick a song from a song database and then press record. When the song ends or from when you press stop, you’ll be directed to a page where you tell us how the song made you feel, and your rating of the song, and then you can save/delete it.

When you press suggest a song, our app can approach this from different perspectives. Muse’s libraries allow us to get the data for each of those different kinds of waves as a value of its amplitude relatively or absolutely, and we can use the average values of these songs to match you with other songs that have similar averages. Another approach is to match the actual pattern of the song, such as the amount of bumps in the brainwaves during the song and matching those attributes, or we could even find similar curves in other songs. In our app, after you press suggest a song, you can select a tag for the feeling you want from the song (this is also data that is averaged, which is stored in the user storing stage). Then muse will use the mentioned algorithms to suggest songs for you!

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