论文标题
部分可观测时空混沌系统的无模型预测
BEAMERS: Brain-Engaged, Active Music-based Emotion Regulation System
论文作者
论文摘要
随着我们日常生活中对情感理解和调节的需求不断增长，通过采用当前的脑电图信息和歌曲功能，引入了定制的基于音乐的情感调节系统，该系统可以预测用户在推荐音乐之前对价值的情感变化。该作品表明：（1）具有商业脑电图设备的新型基于音乐的情感调节系统，而无需使用确定性的情感识别模型来日常使用； （2）该系统认为用户对同一首歌的变体情绪，并通过计算用户的情绪不稳定性，并且符合五大人格测试； （3）该系统以用户指定所需的情绪变化的指定支持不同的情绪调节样式，并在2秒钟的脑电图数据中获得了超过$ 0.85 $的准确性； （4）人们更容易报告自己的情绪变化与绝对情绪状态相比，并且会根据问卷调节情绪调节的更精致的音乐推荐系统。
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.
