BEAMERS: Brain-Engaged, Active Music-based Emotion Regulation System

TL;DR

This paper presents BEAMERS, a music-based emotion regulation system that uses EEG data to predict and adapt to users' emotional states, enabling personalized music recommendations without relying on deterministic emotion recognition models.

Contribution

The work introduces a novel EEG-based music emotion regulation system that accounts for individual emotion variability and supports customizable regulation styles, achieving high accuracy in real-time predictions.

Findings

Achieves over 0.85 accuracy with 2-second EEG data

Supports multiple emotion regulation styles based on user preferences

Users find it easier to report emotion changes than absolute states

Abstract

With the increasing demands of emotion comprehension and regulation in our daily life, a customized music-based emotion regulation system is introduced by employing current EEG information and song features, which predicts users' emotion variation in the valence-arousal model before recommending music. The work shows that: (1) a novel music-based emotion regulation system with a commercial EEG device is designed without employing deterministic emotion recognition models for daily usage; (2) the system considers users' variant emotions towards the same song, and by which calculate user's emotion instability and it is in accordance with Big Five Personality Test; (3) the system supports different emotion regulation styles with users' designation of desired emotion variation, and achieves an accuracy of over $0.85$ with 2-seconds EEG data; (4) people feel easier to report their emotion variation comparing with absolute emotional states, and would accept a more delicate music recommendation system for emotion regulation according to the questionnaire.