Towards a brain controller interface for generating simple Berlin School style music with interactive genetic algorithms

A novel approach to generating music is presented using two interactive Genetic Algorithms with electroencephalogram inputs from two subjects as their fitness functions. Many interactive Genetic Algorithm approaches for generating music employ constrained solution spaces that only utilise notes from a given scale. Our work incorporates the use of mutation to extend the solution space through the inclusion of accidental notes. A thresholding approach is adopted, that allows riffs to be repeated until fitness drops, together with a ‘killswitch’ to ensure unpleasant sounding riffs are removed from the population.
The development is ongoing, with more testing and calibration required to ensure that there are no timing errors in communication between the microcontroller boards and to identify the most appropriate threshold and mutation ranges, in addition to determining the most appropriate mixes for the users to hear.

This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-91100-3_31. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-ma...

Short Application Stream Paper presented as a poster at the conference