A review: Music-emotion recognition and analysis based on EEG signals

被引:6
|
作者
Cui, Xu [1 ]
Wu, Yongrong [2 ]
Wu, Jipeng [3 ]
You, Zhiyu [4 ]
Xiahou, Jianbing [5 ]
Ouyang, Menglin [6 ]
机构
[1] Xiamen Univ, Art Sch, Xiamen, Peoples R China
[2] Xiamen Univ, Dept Software Engn, Xiamen, Peoples R China
[3] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[4] Xiamen Univ, Natl Inst Data Sci Hlth & Med, Sch Med, Xiamen, Peoples R China
[5] Quanzhou Normal Univ, Math & Comp Sch, Quanzhou, Peoples R China
[6] Ningbo Univ, Affiliated Hosp Med Sch, Ningbo, Peoples R China
关键词
EEG; emotions; music; recognition; song; CLASSIFICATION ALGORITHM; REGRESSION; RELEVANT; DYNAMICS; MODEL;
D O I
10.3389/fninf.2022.997282
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.
引用
收藏
页数:17
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