Title: Design and Initial Assessment of a Streamlined Brain-Computer Musical Interface for Real-Time, Emotion-Responsive Sonification

Abstract

This study introduces a streamlined Brain-Computer Musical Interface (BCMI) designed to operate as a real-time affective sonification platform. The system converts prefrontal EEG signals into adaptive musical output by estimating emotional valence through frontal alpha asymmetry measured at the AF7 and AF8 electrodes. These valence estimates drive a stochastic generative algorithm, which modulates specific musical parameters including mode, tempo, rhythmic density, and pitch register. The technical architecture combines wireless EEG data acquisition, real-time signal processing via Python, and music generation within Ableton Live, with synchronization managed through the Lab Streaming Layer.

To evaluate the system’s responsiveness, an experiment involving 22 participants was conducted to determine if intentional emotional self-induction could influence the BCMI’s neurofeedback signal. Linear mixed-effects modeling revealed no significant impact from either the targeted emotion or the passage of time, suggesting that the frontal alpha asymmetry metric failed to consistently differentiate between instructed emotional states. Notably, individual characteristics such as prior musical training and acting experience accounted for a greater share of the variance than the experimental conditions themselves, which explained merely 0.40% of the total signal variance. These results underscore the difficulties associated with utilizing frontal alpha asymmetry as a voluntary control mechanism for closed-loop emotion regulation and offer insights for methodological improvements in future BCMI investigations.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC