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

TL;DR

This paper presents a novel BCI system that uses EEG data and machine learning to automatically evaluate music based on listener brainwaves, aiming to enhance music annotation in streaming services.

Contribution

It introduces a new BCI framework combining neuroscientific features and extreme learning machines for personalized music evaluation.

Findings

Successful translation of EEG features into listener scores

Effective feature selection based on brainwave perturbations

Promising results in automated music annotation

Abstract

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.