Qiu Lina, Zhong Yongshi, Xie Qiuyou, He Zhipeng, Wang Xiaoyun, Chen Yingyue, Zhan Chang'an A, Pan Jiahui
School of Software, South China Normal University, Guangzhou, China.
Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Front Neurorobot. 2022 Jan 31;16:823435. doi: 10.3389/fnbot.2022.823435. eCollection 2022.
Music can effectively improve people's emotions, and has now become an effective auxiliary treatment method in modern medicine. With the rapid development of neuroimaging, the relationship between music and brain function has attracted much attention. In this study, we proposed an integrated framework of multi-modal electroencephalogram (EEG) and functional near infrared spectroscopy (fNIRS) from data collection to data analysis to explore the effects of music (especially personal preferred music) on brain activity. During the experiment, each subject was listening to two different kinds of music, namely personal preferred music and neutral music. In analyzing the synchronization signals of EEG and fNIRS, we found that music promotes the activity of the brain (especially the prefrontal lobe), and the activation induced by preferred music is stronger than that of neutral music. For the multi-modal features of EEG and fNIRS, we proposed an improved Normalized-ReliefF method to fuse and optimize them and found that it can effectively improve the accuracy of distinguishing between the brain activity evoked by preferred music and neutral music (up to 98.38%). Our work provides an objective reference based on neuroimaging for the research and application of personalized music therapy.
音乐可以有效改善人们的情绪，现已成为现代医学中一种有效的辅助治疗方法。随着神经成像技术的快速发展，音乐与脑功能之间的关系备受关注。在本研究中，我们提出了一个从数据采集到数据分析的多模态脑电图（EEG）和功能近红外光谱（fNIRS）综合框架，以探索音乐（特别是个人喜爱的音乐）对大脑活动的影响。实验过程中，每个受试者聆听两种不同的音乐，即个人喜爱的音乐和中性音乐。在分析EEG和fNIRS的同步信号时，我们发现音乐能促进大脑（尤其是前额叶）的活动，且喜爱的音乐诱发的激活比中性音乐更强。针对EEG和fNIRS的多模态特征，我们提出了一种改进的归一化 ReliefF 方法对其进行融合和优化，发现该方法能有效提高区分喜爱的音乐和中性音乐诱发的大脑活动的准确率（高达98.38%）。我们的工作为个性化音乐疗法的研究和应用提供了基于神经成像的客观参考。
