“mico” frees the user from having to select songs and artists and allows users to encounter new music just by wearing the device.
The mico system is made up of two parts. the mico headphone, and the mico app for iPhone. The mico headphone detects brainwaves through the sensor on your forehead. The mico app then automatically analyzes the user’s condition of the brain,and searches for music that best matches from the mico music data base, and plays the selection that fits the user’s status.
mico provides a new experience which we call “Music Serendipity”, by detecting the users subconsciousness through their brainwaves.
“mico” is short for “music inspiration from your subconsciousness”.
mico music database (prototype) contains 100 songs that have been “neuro-tagged” by testing several people’s brain reaction to those songs. Each song is associated to a set of characteristics modeling that song.
The sensor on the forehead detects and analyzes the user brainwaves, then matches them to the closest “neural pattern” pre-recorded in the database to determine the user “neural group”. After determining the user neural group, the algorithm analyzes the user latest brainwaves to determine a set of music characteristics that will best match the user’s mood. The system then looks up the database to match the user with the song that has the closest neural pattern, and actually plays the song.
The algorithm was co-developed by neurowear team from Dentsu Lab Tokyo and the Mitsukura Laboratory of Keio University. The main developers of the algorithm are Ms. Mitsukura and Mr. Ogino, respectively Professor and Researcher at the Mitsukura Laboratory.
- Creative Director/Planner：Yasuhiro Tsuchiya(Dentsu Lab Tokyo)｜Producer：Toshitaka Kamiya(Dentsu Lab Tokyo)｜Planner：Kana Nakano(Dentsu Lab Tokyo)｜Planner：Tomonori Kagaya(neurowear) | Product Design/Design Engineering：Hajime Kuge(tsug) | Design Engineering：Atsushi Sasaki(tsug) | Product Design/Design Engineering：Ippei Iwahara｜Software Development：Nao Tokui(Qosmo)｜EEG Analysis/Algorithm Development：Yasue Mitsukura(Keio University Mitsukura Lab), Mikito Ogino(Keio University Mitsukura Lab)