A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service “Taking Listener’s Brainwaves to Extremes”

  • Fotis KalaganisEmail author
  • Dimitrios A. Adamos
  • Nikos Laskaris
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 475)


We investigated the possibility of a 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 extreme learning machines (ELMs) is exploited so as to translate the derived patterns into a listener’s score. Encouraging experimental results, from a pragmatic use of the system, are presented.


EEG Music evaluation Recommendation-systems Human machine interaction Spotify 


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Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Fotis Kalaganis
    • 1
    Email author
  • Dimitrios A. Adamos
    • 2
    • 3
  • Nikos Laskaris
    • 1
    • 3
  1. 1.AIIA Lab, Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece
  2. 2.School of Music StudiesAristotle University of ThessalonikiThessalonikiGreece
  3. 3.Neuroinformatics GRoupAristotle University of ThessalonikiThessalonikiGreece

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