  We investigated the possibility of using a machine-learning scheme in
conjunction with commercial wearable EEG-devices for translating listener's
subjective experience of music into scores that can be used for the automated
annotation of music in popular on-demand streaming services. Based on the
established -neuroscientifically sound- concepts of brainwave frequency bands,
activation asymmetry index and cross-frequency-coupling (CFC), we introduce a
Brain Computer Interface (BCI) system that automatically assigns a rating score
to the listened song. Our research operated in two distinct stages: i) a
generic feature engineering stage, in which features from signal-analytics were
ranked and selected based on their ability to associate music induced
perturbations in brainwaves with listener's appraisal of music. ii) a
personalization stage, during which the efficiency of ex- treme learning
machines (ELMs) is exploited so as to translate the derived pat- terns into a
listener's score. Encouraging experimental results, from a pragmatic use of the
system, are presented.
