Research on the Influence of Classical Music and Rock Music on Working Memory Based on the Brain Network
Li Ying1,2#*, Zhang Dongying1, Su Qing2, Li Jipeng1, Yan Wei1,2
1.(State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China) 2.(Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China)
Abstract：Music is a medium for people to convey information and express their feelings, and meanwhile it has a great influence on the cognitive activities. In this paper, the improved Sternberg working memory task experiment was designed, and 20 subjects were selected to participate in the experiment. The EEG data of subjects in remembering English words under the background of classical music and rock music were collected and the cross-correlation method was used to calculate Pearson between channels. The brain function networks were built and the network properties were analyzed to explore the impact on working memory of two kinds of music stimulations. Results showed that the connections between the frontal lobe and the occipital lobe in the state of classical music and rock music were significantly increased compared to that in the state without music, and the connection in the state of rock music was significantly greater than that in the state of classical music. Compared to the brain functional network in the state without music, rock music and classical music stimulations increased the node degree, betweenness center degree, clustering coefficient and closeness, shortened the average path length, enhanced the small world property and improved the global efficiency. It was shown that the music stimulations improved the activity of the brain. It was proved that different music stimulations have significant impacts on the learning task. The results of behavioral data showed that the correct response rates of the subjects under no music stimulation, classical music state and rock music state were about 91%, 86% and 80%, respectively, and the average response times under three states were 728 ms, 761 ms and 818 ms, respectively. It could be seen that the music stimulations reduced the correct response rates of the subjects and increased the response times, which indicated that the musical stimulations interfere with the working memory.
作者简介: #中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)
引用本文:
李颖, 张东颖, 苏晴, 李继鹏, 闫伟. 基于脑网络的古典音乐和摇滚音乐对工作记忆的影响研究[J]. 中国生物医学工程学报, 2019, 38(2): 129-137.
Li Ying, Zhang Dongying, Su Qing, Li Jipeng, Yan Wei. Research on the Influence of Classical Music and Rock Music on Working Memory Based on the Brain Network. Chinese Journal of Biomedical Engineering, 2019, 38(2): 129-137.
[1] George EM, Coch D. Music training and working memory: an ERP study [J]. Neuropsychologia, 2011, 49(5): 1083-1094. [2] 王杭,江俊,蒋存梅. 音乐训练对认知能力的影响 [J]. 心理科学进展, 2015, 23(3): 419-429. [3] Ericsson KA, Kintsch W. Long-term working memory[J]. Psychological Review, 1995, 102(2): 211-245. [4] Bo H, Li H, Ma L, et al. Time-course EEG spectrum evidence for music key perception and emotional effects[M]// Advances in Brain Inspired Cognitive Systems. Beilin: Springer International Publishing, 2016. [5] Banerjee A, Sanyal S, Patranabis A. Study on brain dynamics by non linear analysis of music induced EEG signals [J]. Statistical Mechanics and its Applications, 2016(444): 110-120. [6] Boersma M, Smit DJA, De Bie HMA, et al. Network analysis of resting state EEG in the developing young brain: Structure comes with maturation [J]. Human Brain Mapping, 2015, 32(3): 413-425. [7] 梁夏,王金辉,贺永. 人脑连接组研究:脑结构网络和脑功能网络 [J]. 科学通报, 2010, 55(16): 1565-1583. [8] 侯建成. “莫扎特效应”的认知神经科学研究 [J]. 中国特殊教育,2007(3): 85-91. [9] 赵会坡. 视觉工作记忆不同负载下脑电网络的信息传递特性研究 [D]. 天津:天津医科大学, 2015. [10] Qin Y, Xu P, Yao D. A comparative study of different references for EEG default mode network: the use of the infinity reference [J]. Clinical Neurophysiology, 2010, 121(12): 1981-1991. [11] Yao D. A method to standardize a reference of scalp EEG recordings to a point at infinity [J]. Physiological Measurement, 2001, 22(4): 693-711. [12] Dong L, Li F, Liu Q, et al. MATLAB Toolboxes for reference electrode standardization technique (REST) of Scalp EEG [J]. Frontiers in Neuroscience, 2017, 11: 601. [13] Buckner RL, Sepulcre J, Talukdar T, et al. Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease [J]. Neuroscience, 2009, 29: 1860-1873. [14] Guo L, Wang Y, Yu HL, et al. Study of brain functional network based on sample entropy of EEG under magnetic stimulation at PC6 acupoint [J]. Bio-Medical Materials and Engineering, 2014, 24(1): 1063-1069. [15] He Y, Che ZJ, Evans AC. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease [J]. Neuroscience, 2008, 28 (18): 4756-4766. [16] Stam CJ. Functional connectivity patterns of human magnetoencephalographic recordings: A “small-world” network [J]. Neuroscience Letters, 2004, 355: 25-28. [17] 尹宁,徐桂芝,周茜. 磁刺激穴位复杂脑功能网络构建与分析 [J]. 物理学报, 2013, 62(11): 559-566. [18] 赵静. 基于互信息的EEG脑功能网络研究 [D]. 西安:西安电子科技大学, 2013. [19] Lehrer J. Neuroscience: Making connections [J]. Nature, 2009 (457): 524-527. [20] Yin ZL, Li J, Zhang Y, et al. Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series [J]. Biomedical Signal Processing and Control, 2017, 31: 331-338. [21] Lu J, Yang H, He H, et al. The multiple-demand system in the novelty of musical improvisation: Evidence from an MRI study on composers [J]. Frontiers in Neuroscience, 2017, 11: 695.
