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.

翻译：本文提出了一种极简的脑机音乐接口（BCMI），该接口作为一个实时情感声音化系统，将前额叶脑电图活动转换为自适应音乐。情感效价通过额叶α不对称性（AF7/AF8）进行估计，并通过随机生成算法映射到音乐特征，如调式、速度、节奏密度和音高音域。该系统集成了无线脑电图采集、实时Python信号处理以及通过Lab Streaming Layer同步的基于Ableton Live的音乐生成。一项包含22名参与者的实验探究了有意识的情感自我诱导是否能够调节BCMI神经反馈信号。线性混合效应分析发现目标情感或时间无显著效应，表明额叶α不对称性信号未能可靠地区分指示性情感状态。个体差异（包括音乐训练和表演经验）比实验操作解释了更多的方差，而实验操作仅占总信号方差的0.40%。这些发现凸显了将额叶α不对称性作为闭环情感调节的自愿控制信号所面临的挑战，并为未来BCMI研究提出了方法论方向。