不同意识状态对音乐感知的差异性:基于音乐特征与脑电张量分解的研究

doi:10.3969/j.issn.0258-8021.2021.03.01

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1824 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要研究不同意识水平对音乐感知的差异性,探索不同意识水平与音乐感知之间的关系。被试共43名,其中7名正常意识被试、17名微意识状态(MCS)被试和19名植物状态(VS)被试。获取3种不同意识水平被试在聆听传统民乐《茉莉花》时的脑电,利用基于层次交替最小二乘法(HALS)非负张量分解,对被试在听音乐时产生的脑电提取张量成分,对这些成分做功率谱比率分析和脑地形图分析;在此基础上,对音乐包含的5种信息学特征的EEG响应进行研究,包括中心化波动值(fluctuation centroid)、波动熵(fluctuation entropy)、音阶清晰值(key clarity)、脉冲清晰值(pulse clarity)和调值(mode)。结果表明:1)正常意识被试、MCS被试和VS被试对5种音乐特征的脑电响应在节律上存在显著差异,正常意识被试的 alpha 波占比高于MCS和VS被试,其中:fluctuation centroid,正常意识被试、MCS被试和VS被试分别为0.687±0.193、0.033±0.022、0.063±0.040(P<0.001);fluctuation entropy,正常意识被试、MCS被试和VS被试分别为0.588±0.132、0.041±0.025、0.085±0.077(P<0.001);key clarity,正常意识被试、MCS被试和VS被试分别为0.668±0.295、0.096±0.103、0.057±0.065(P<0.001);pulse clarity,正常意识被试、MCS被试和VS被试分别为0.672±0.064、0.144±0.242、0.044±0.044(P<0.001);mode,正常意识被试、MCS被试和VS被试分别为0.432±0.273、0.057±0.049、0.033±0.026(P<0.001)。除mode特征外,beta波与alpha波体现相同的统计特征(P<0.05)。2) MCS和VS被试对音乐特征的响应在节律上没有差异,均以delta波和theta波的慢波为主。3)在响应激活区域上,正常意识被试对5种音乐特征的响应主要分布于前额叶,MCS和VS被试的响应主要分布在颞叶。研究表明,不同意识水平对大脑加工音乐特征有影响,这为音乐感知与意识关系探索提供新的模型和研究范式。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	梅戬
	王小宇
	刘杨
	李景琦
	刘克洪
	杨勇
	丛丰裕

关键词 ：脑电信号, 音乐感知, 张量分解, 意识水平

Abstract：In this study, we explored the relationship between different levels of consciousness and music processing. We used the EEG data of 43 participants, including 7 normal participants, 17 minimally conscious state (MCS) participants and 19 vegetative state (VS) participants. During the experiment, participants were requested to listen to the traditional folk music “Jasmine”. We extracted tensor components from the EEG by using hierarchical alternating least squares (HALS) non-negative tensor decomposition. After that, we selected components correlated with the five music informatic features (fluctuation centroid, fluctuation entropy, key clarity, pulse clarity and mode) respectively. Then power spectrum ratio analysis and brain topography analysis were applied to the components selected to study the EEG response. The results showed that: 1) differences existed in the proportion of alpha waves of the EEG response to the 5 music features among the normal group, the MCS group and the VS group: fluctuation centroid (normal group 0.687±0.193, MCS group 0.033±0.022, VS group 0.063±0.040, P<0.001), fluctuation entropy (normal group 0.588±0.132, MCS group 0.041±0.025, VS group 0.085±0.077, P<0.001), key clarity (normal group 0.668±0.295, MCS group 0.096±0.103, VS group 0.057±0.065, P<0.001), pulse clarity (normal group 0.672±0.064, MCS group 0.144±0.242, VS group 0.044±0.044, P<0.001), mode (normal group 0.432±0.273, MCS group 0.057±0.049, VS group 0.033±0.026, P<0.001).Except feature mode, beta wave of the EEG response to the rest 4 music features have the same statistical results as alpha wave (P＜0.05). 2) The rhythm of the response of MCS group and VS group to the 5 music features was mainly the slow wave of delta wave and theta wave. 3) As for the EEG activation area, the normal group EEG response to the 5 music features mainly distributed in frontal lobe but the MCS and VS group EEG response mainlyin parietal lobe. The results showed that the level of consciousness had effects on brain processing music feature, which provided a new research paradigm for exploring the relation between level of consciousness and music processing.

Key words： EEG music processing tensor decomposition level of consciousness

收稿日期: 2020-10-10

PACS:

R318

基金资助:国家自然科学基金(81671038)

通讯作者: * E-mail: yyang@hdu.edu.cn;cong@dlut.edu.cn

引用本文:

梅戬, 王小宇, 刘杨, 李景琦, 刘克洪, 杨勇, 丛丰裕. 不同意识状态对音乐感知的差异性:基于音乐特征与脑电张量分解的研究[J]. 中国生物医学工程学报, 2021, 40(3): 257-265.
Mei Jian, Wang Xiaoyu, Liu Yang, Li Jingqi, Liu Kehong, Yang Yong, Cong Fengyu. The Difference of Music Processing among Different State of Consciousness: A Study Based on Music Features and EEG Tensor Decomposition. Chinese Journal of Biomedical Engineering, 2021, 40(3): 257-265.

链接本文:

http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2021.03.01 或 http://cjbme.csbme.org/CN/Y2021/V40/I3/257

[1] Koelsch S. Toward a neural basis of music perception-A review and updated model [J]. Frontier in Psychology, 2011, 2: 110.
[2] Schwenzer M, Mathiak K. Numeric aspects in pitch identification: An fMRI study [J]. BMC Neuroscience, 2011, 12: 26.
[3] 赵怀阳,江俊,周临舒,等. 人类镜像系统参与音乐情绪的自动加工: 来自EEG的证据[J]. 心理学报, 2019, 51(7): 795-804.
[4] Piccirilli M, Sciarma T, Luzzi S. Modularity of music: Evidence from a case of pure amusia [J]. Journal of Neurology, 2000, 69(4): 541-545.
[5] OKelly J, James L, Palaniappan R, et al. Neurophysiological and behavioral responses to music therapy in vegetative and minimally conscious states [J]. Frontiers in Human Neuroscience, 2013,7: 884.
[6] Boly M, Garrido MI, Gosseries O, et al. Preserved feedforward but impaired top-down processes in the vegetative state [J]. Science, 2011, 332(6031): 858-862.
[7] Crone JS, Ladurner G, Holler Y, et al. Deactivation of the default mode network as a marker of impaired consciousness: An fMRI study [J]. PLoS ONE, 2011, 6(10): e26373.
[8] Cong F, Phan A H, Zhao Q, et al. Analysis of ongoing EEG elicited by natural music stimuli using nonnegative tensor factorization[C]//2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO). Bucharest: IEEE, 2012: 494-498.
[9] Cichocki A, Zdunek R, Phan AH, et al. Multi-way array (tensor) factorizations and decompositions [M]//Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. West Sussex: John Wiley & Sons, 2009: 337-432.
[10] Cong F, Lin Q, Kuang L, et al. Tensor decomposition of EEG signals: A brief review [J]. Journal of Neuroscience Methods, 2015, 248: 59-69.
[11] Wang D, Cong F, Zhao Q, et al. Exploiting ongoing EEG with multilinear partial least squares during free-listening to music [C]// 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). Vietrisul Mare: IEEE, 2016: 1-6.
[12] Zhao Q, Caiafa CF, Mandic D, et al. Multilinear subspace regression: An orthogonal tensor decomposition approach [J]. Advances in Neural Information Processing Systems, 2011, 24: 1269-1277.
[13] Alluri V, Toiviainen P, Jääskeläinen IP, et al. Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm [J]. NeuroImage, 2012, 59(4): 3677-3689.
[14] Urbenjaphol P, Jitpanya C, Khaoropthum S. Effects of the sensory stimulation program on recovery in unconscious patients with traumatic brain injury[J]. Journal of Neuroscience Nursing, 2009, 41(3): E10-E16.
[15] Lartillot O, Toiviainen P. MIR in matlab (II): A toolbox for musical feature extraction from audio [C]// The 8th International Conference on Music Information Retrieval. Vienna: ISMIR, 2007: 237-244.
[16] Cong F, Alluri V, Nandi AK, et al. Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features [J]. IEEE Transactions on Multimedia, 2013, 15(5): 1060-1069.
[17] Cichocki A, Lee N, Oseledets I, et al. Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives [J]. Foundations & Trends in Machine Learning, 2017, 9(6): 249-429.
[18] Phan AH, Cichocki A. Multi-way nonnegative tensor factorization using fast hierarchical alternating least squares algorithm (HALS) [C]// The 2008 International Symposium on Nonlinear Theory and its Applications. Budapest: IEICE, 2008: 41-44.
[19] Wang D, Zhu Y, Ristaniemi T, et al. Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition [J]. Journal of Neuroscience Methods, 2018, 308: 240-247.
[20] Arnaud D, Jason P, Julie O, et al. Independent EEG sources are dipolar [J]. PLoS ONE, 2012,7(2):e30135.
[21] Fernández T, Harmony T, Rodríguez M, et al. EEG activation patterns during the performance of tasks involving different components of mental calculation[J]. Electroencephalography and Clinical Neurophysiology, 1995, 94(3):175-182.
[22] 赖寒,徐苗,宋宜颖,等. 音乐知觉的神经基础:脑成像研究的元分析[J]. 心理学报, 2013, 45(5): 491-507.