A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation

2026-05-31

AI Summary

This study investigates whether prefrontal alpha asymmetry can serve as a reliable neural correlate of emotional valence to drive adaptive music generation. To this end, the authors developed a minimalist brain–computer music interface that acquires EEG signals wirelessly in real time, processes alpha asymmetry using Python, and maps it onto musical parameters—such as mode, tempo, rhythmic density, and pitch—within a closed-loop system integrating Ableton Live and Lab Streaming Layer. Experimental results revealed that targeted emotional induction exerted only a negligible effect on alpha asymmetry (accounting for merely 0.40% of variance), whereas individual differences, including musical training, had a substantially stronger influence, highlighting limitations of this metric in active control scenarios. Challenging high-dimensional brain–computer interface paradigms, this work proposes a lightweight, deployable framework for emotion-driven music generation.

Abstract

This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system, translating prefrontal EEG activity into adaptive music. Emotional valence is estimated from frontal alpha asymmetry (AF7/AF8) and mapped to musical features such as mode, tempo, rhythmic density, and pitch register through a stochastic generative algorithm. The system integrates wireless EEG acquisition, real-time Python signal processing, and Ableton Live-based music generation synchronized via Lab Streaming Layer. An experiment with 22 participants investigated whether intentional emotional self-induction could modulate the BCMI neurofeedback signal. Linear mixed-effects analyses found no significant effects of target emotion or time, indicating that the frontal alpha asymmetry signal did not reliably distinguish instructed emotional states. Individual differences, including musical training and acting experience, explained more variance than the experimental manipulation, which accounted for only 0.40\% of total signal variance. These findings highlight the challenges of using frontal alpha asymmetry as a voluntary control signal for closed-loop emotion regulation and suggest methodological directions for future BCMI research.

Problem

• brain-computer interface
• emotion regulation
• frontal alpha asymmetry
• affective sonification
• real-time neurofeedback

Innovation

• brain-computer musical interface
• affective sonification
• frontal alpha asymmetry
• real-time EEG processing
• stochastic music generation