文献类型：期刊文献

英文题名：Music-oriented auditory attention detection from electroencephalogram

作者：Niu, Yixiang[1];Chen, Ning[1];Zhu, Hongqing[1];Jin, Jing[2,3];Li, Guangqiang[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China

年份：2024

卷号：818

外文期刊名：NEUROSCIENCE LETTERS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001108252400001)】；

基金：Acknowledgements This work was supported by the National Natural Science Foundation of China [grant numbers 61771196, 61872143] . The authors would like to express sincere gratitude to the associate editor and the reviewers for their careful review.

语种：英文

外文关键词：Auditory attention detection; Electroencephalogram; Audio feature fusion; Common spatial pattern; Structural similarity index

摘要：Music-oriented auditory attention detection (AAD) aims at determining which instrument in polyphonic music a listener is paying attention to by analyzing the listener's electroencephalogram (EEG). However, the existing linear models cannot effectively mimic the nonlinearity of the human brain, resulting in limited performance. Thus, a nonlinear music-oriented AAD model is proposed in this paper. Firstly, an auditory feature and a musical feature are fused to represent musical sources precisely and comprehensively. Secondly, the EEG is enhanced if music stimuli are presented in stereo. Thirdly, a neural network architecture is constructed to capture nonlinear and dynamic interactions between the EEG and auditory stimuli. Finally, the musical source most similar to the EEG in the common embedding space is identified as the attended one. Experimental results demonstrate that the proposed model outperforms all baseline models. On 1-s decision windows, it reaches accuracies of 92.6% and 81.7% under mono duo and trio stimuli, respectively. Additionally, it can be easily extended to speech-oriented AAD. This work can open up new possibilities for studies on both brain neural activity decoding and music information retrieval.