Cross-subject aesthetic preference recognition of Chinese dance posture using EEG

• Published in: Cognitive neurodynamics Vol. 17; no. 2; pp. 311 - 329
• Main Authors: Li, Jing, Wu, Shen-rui, Zhang, Xiang, Luo, Tian-jian, Li, Rui, Zhao, Ying, Liu, Bo, Peng, Hua
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2023; Springer Nature B.V
• Subjects: Ablation; Aesthetics; Artificial Intelligence; Artificial neural networks; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Brain research; Cerebral hemispheres; Cognition; Cognitive Psychology; Computer Science; Dance; Design; EEG; Electroencephalography; Folk dancing; Hemispheres; Information processing; Model accuracy; Neural networks; Neurosciences; Posture; Preferences; Questionnaires; Recognition; Research Article; Transfer learning; Aesthetic preference; Chinese dance posture; Electroencephalogram; Convolutional neural network; Cross-subject transfer
• DOI: 10.1007/s11571-022-09821-2
• ISSN: 1871-4080

Due to the differences in knowledge, experience, background, and social influence, people have subjective characteristics in the process of dance aesthetic cognition. To explore the neural mechanism of the human brain in the process of dance aesthetic preference, and to find a more objective determining criterion for dance aesthetic preference, this paper constructs a cross-subject aesthetic preference recognition model of Chinese dance posture. Specifically, Dai nationality dance (a classic Chinese folk dance) was used to design dance posture materials, and an experimental paradigm for aesthetic preference of Chinese dance posture was built. Then, 91 subjects were recruited for the experiment, and their EEG signals were collected. Finally, the transfer learning method and convolutional neural networks were used to identify the aesthetic preference of the EEG signals. Experimental results have shown the feasibility of the proposed model, and the objective aesthetic measurement in dance appreciation has been implemented. Based on the classification model, the accuracy of aesthetic preference recognition is 79.74%. Moreover, the recognition accuracies of different brain regions, different hemispheres, and different model parameters were also verified by the ablation study. Additionally, the experimental results reflected the following two facts: (1) in the visual aesthetic processing of Chinese dance posture, the occipital and frontal lobes are more activated and participate in dance aesthetic preference; (2) the right brain is more involved in the visual aesthetic processing of Chinese dance posture, which is consistent with the common knowledge that the right brain is responsible for processing artistic activities.