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

TL;DR

This paper presents two machine learning methods, NeuroPicks and NeuroPicksVQ, that use EEG data to translate listeners' subjective music experiences into scores for personalized music streaming, balancing accuracy and speed.

Contribution

It introduces novel EEG-based scoring methods for music recommendation and selection, leveraging neuroscientific features and extreme learning machines for personalized streaming applications.

Findings

NeuroPicks achieves high accuracy in predicting music scores.

NeuroPicksVQ offers faster predictions with moderate accuracy.

Both methods demonstrate promising results in practical streaming scenarios.

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 in popular on-demand music streaming services. Our study resulted into two variants, differing in terms of performance and execution time, and hence, subserving distinct applications in online streaming music platforms. The first method, NeuroPicks, is extremely accurate but slower. It is based on the well-established neuroscientific concepts of brainwave frequency bands, activation asymmetry index and cross frequency coupling (CFC). The second method, NeuroPicksVQ, offers prompt predictions of lower credibility and relies on a custom-built version of vector quantization procedure that facilitates a novel parameterization of the music-modulated brainwaves. Beyond the feature engineering step, both methods exploit the inherent efficiency of extreme learning machines (ELMs) so as to translate, in a personalized fashion, the derived patterns into a listener's score. NeuroPicks method may find applications as an integral part of contemporary music recommendation systems, while NeuroPicksVQ can control the selection of music tracks. Encouraging experimental results, from a pragmatic use of the systems, are presented.