用脑电波实时生成情绪音乐，但实验发现效果不显著。
A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation
- 通过前额叶α波不对称性映射情绪，生成自适应音乐。
- 22人实验显示情绪指令对脑电信号影响极小（仅解释0.4%方差）。
- 适合关注脑机接口音乐生成与情绪调控的科研人员。
本文提出一种极简脑-计算机音乐接口（BCMI），可将前额叶脑电活动实时转化为自适应音乐。通过检测AF7/AF8电极的α波不对称性估计情绪效价，并利用随机生成算法将其映射为调式、速度、节奏密度和音高范围等音乐特征。系统集成无线脑电采集、Python实时信号处理及基于Ableton Live的音乐生成，通过Lab Streaming Layer实现同步。22名参与者参与实验，探究主动情绪诱导能否调节神经反馈信号。线性混合效应模型分析表明，目标情绪或时间均无显著影响，前额叶α波不对称性无法可靠区分受试者被指令的情绪状态。个体差异（如音乐训练、表演经验）解释的方差远超实验操纵（后者仅占总方差的0.40%）。研究揭示了以α波不对称性作为自愿控制信号在闭环情绪调节中的局限性，并为未来BCMI研究提供了方法学方向。
原文摘要 · Abstract (English)
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.
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