Xie Zun, Pan Jianwei, Li Songjie, Ren Jing, Qian Shao, Ye Ye, Bao Wei
Department of Arts and Design, Anhui University of Technology, Ma'anshan 243002, China.
Department of Management Science and Engineering, Anhui University of Technology, Ma'anshan 243002, China.
Entropy (Basel). 2022 Nov 28;24(12):1735. doi: 10.3390/e24121735.
The dynamic of music is an important factor to arouse emotional experience, but current research mainly uses short-term artificial stimulus materials, which cannot effectively awaken complex emotions and reflect their dynamic brain response. In this paper, we used three long-term stimulus materials with many dynamic emotions inside: the "Waltz No. 2" containing pleasure and excitement, the "No. 14 Couplets" containing excitement, briskness, and nervousness, and the first movement of "Symphony No. 5 in C minor" containing passion, relaxation, cheerfulness, and nervousness. Approximate entropy (ApEn) and sample entropy (SampEn) were applied to extract the non-linear features of electroencephalogram (EEG) signals under long-term dynamic stimulation, and the K-Nearest Neighbor (KNN) method was used to recognize emotions. Further, a supervised feature vector dimensionality reduction method was proposed. Firstly, the optimal channel set for each subject was obtained by using a particle swarm optimization (PSO) algorithm, and then the number of times to select each channel in the optimal channel set of all subjects was counted. If the number was greater than or equal to the threshold, it was a common channel suitable for all subjects. The recognition results based on the optimal channel set demonstrated that each accuracy of two categories of emotions based on "Waltz No. 2" and three categories of emotions based on "No. 14 Couplets" was generally above 80%, respectively, and the recognition accuracy of four categories based on the first movement of "Symphony No. 5 in C minor" was about 70%. The recognition accuracy based on the common channel set was about 10% lower than that based on the optimal channel set, but not much different from that based on the whole channel set. This result suggested that the common channel could basically reflect the universal features of the whole subjects while realizing feature dimension reduction. The common channels were mainly distributed in the frontal lobe, central region, parietal lobe, occipital lobe, and temporal lobe. The channel number distributed in the frontal lobe was greater than the ones in other regions, indicating that the frontal lobe was the main emotional response region. Brain region topographic map based on the common channel set showed that there were differences in entropy intensity between different brain regions of the same emotion and the same brain region of different emotions. The number of times to select each channel in the optimal channel set of all 30 subjects showed that the principal component channels representing five brain regions were Fp1/F3 in the frontal lobe, CP5 in the central region, Pz in the parietal lobe, O2 in the occipital lobe, and T8 in the temporal lobe, respectively.
音乐的动态性是唤起情感体验的重要因素，但目前的研究主要使用短期人工刺激材料，无法有效唤醒复杂情感并反映其动态脑反应。在本文中，我们使用了三种蕴含多种动态情感的长期刺激材料：包含愉悦和兴奋的《第二号华尔兹》、包含兴奋、轻快和紧张的《十四首联弹》，以及包含激情、放松、愉悦和紧张的《C小调第五交响曲》第一乐章。应用近似熵（ApEn）和样本熵（SampEn）来提取长期动态刺激下脑电图（EEG）信号的非线性特征，并使用K近邻（KNN）方法进行情感识别。此外，还提出了一种有监督的特征向量降维方法。首先，通过粒子群优化（PSO）算法获得每个受试者的最优通道集，然后统计所有受试者最优通道集中每个通道被选中的次数。若次数大于或等于阈值，则为适用于所有受试者的公共通道。基于最优通道集的识别结果表明，基于《第二号华尔兹》的两类情感以及基于《十四首联弹》的三类情感的各自准确率总体上分别高于80%，基于《C小调第五交响曲》第一乐章的四类情感的识别准确率约为70%。基于公共通道集的识别准确率比基于最优通道集的低约10%，但与基于全通道集的识别准确率差异不大。这一结果表明，公共通道在实现特征降维的同时基本能够反映所有受试者的普遍特征。公共通道主要分布在额叶、中央区、顶叶、枕叶和颞叶。分布在额叶的通道数量多于其他区域，表明额叶是主要的情感反应区域。基于公共通道集的脑区地形图显示，同一情感的不同脑区以及不同情感的同一脑区之间在熵强度上存在差异。对30名受试者最优通道集中每个通道被选中的次数统计结果表明，代表五个脑区的主成分通道分别为额叶的Fp1/F3、中央区的CP5、顶叶的Pz、枕叶的O2和颞叶的T8。
