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We investigated the possibility of using a machine-learning scheme inconjunction with commercial wearable EEG-devices for translating listener'ssubjective experience of music into scores that can be used in popularon-demand music streaming services. Our study resulted into two variants,differing in terms of performance and execution time, and hence, subservingdistinct applications in online streaming music platforms. The first method,NeuroPicks, is extremely accurate but slower. It is based on thewell-established neuroscientific concepts of brainwave frequency bands,activation asymmetry index and cross frequency coupling (CFC). The secondmethod, NeuroPicksVQ, offers prompt predictions of lower credibility and relieson a custom-built version of vector quantization procedure that facilitates anovel parameterization of the music-modulated brainwaves. Beyond the featureengineering step, both methods exploit the inherent efficiency of extremelearning machines (ELMs) so as to translate, in a personalized fashion, thederived patterns into a listener's score. NeuroPicks method may findapplications as an integral part of contemporary music recommendation systems,while NeuroPicksVQ can control the selection of music tracks. Encouragingexperimental results, from a pragmatic use of the systems, are presented.

Musical NeuroPicks: a consumer-grade BCI for on-demand music streaming services