[论文解读] Music of Brain and Music on Brain: A Novel EEG Sonification approach

Sayan Nag, Shankha Sanyal+3 more|arXiv (Cornell University)|Dec 22, 2017

Complex Systems and Time Series Analysis参考文献 15被引用 3

一句话总结

本研究提出了一种新颖的脑电信号（EEG）音色化方法，以探索大脑活动（EEG）与外部音乐刺激之间的直接相关性，采用多分形去趋势互相关分析（MFDXA）。研究结果表明，音乐刺激独特地激活了多个大脑区域，音色化EEG与塔普拉持续音之间表现出显著的互相关性，揭示了非音乐刺激下未观察到的特异性神经生理反应。

ABSTRACT

Can we hear the sound of our brain? Is there any technique which can enable us to hear the neuro-electrical impulses originating from the different lobes of brain? The answer to all these questions is YES. In this paper we present a novel method with which we can sonify the Electroencephalogram (EEG) data recorded in rest state as well as under the influence of a simplest acoustical stimuli - a tanpura drone. The tanpura drone has a very simple yet very complex acoustic features, which is generally used for creation of an ambiance during a musical performance. Hence, for this pilot project we chose to study the correlation between a simple acoustic stimuli (tanpura drone) and sonified EEG data. Till date, there have been no study which deals with the direct correlation between a bio-signal and its acoustic counterpart and how that correlation varies under the influence of different types of stimuli. This is the first of its kind study which bridges this gap and looks for a direct correlation between music signal and EEG data using a robust mathematical microscope called Multifractal Detrended Cross Correlation Analysis (MFDXA). For this, we took EEG data of 10 participants in 2 min 'rest state' (i.e. with white noise) and in 2 min 'tanpura drone' (musical stimulus) listening condition. Next, the EEG signals from different electrodes were sonified and MFDXA technique was used to assess the degree of correlation (or the cross correlation coefficient) between tanpura signal and EEG signals. The variation of γx for different lobes during the course of the experiment also provides major interesting new information. Only music stimuli has the ability to engage several areas of the brain significantly unlike other stimuli (which engages specific domains only).

研究动机与目标

• 探究神经电活动（EEG）与声学刺激（特别是塔普拉持续音）之间的直接相关性。
• 开发并应用一种新颖的EEG音色化技术，将脑信号转化为可听声音以供感知分析。
• 利用多分形互相关分析评估不同脑区对音乐刺激与非音乐刺激的响应差异。
• 识别音乐是否独特地激活多个脑叶，而非其他刺激所引发的孤立领域特异性反应。
• 为利用MFDXA等数学工具研究脑-音乐互动提供一种新的方法论框架。

提出的方法

• 在两种条件下从10名参与者采集EEG数据：2分钟静息状态（伴有白噪音）和2分钟聆听塔普拉持续音。
• 通过音色化过程将不同电极采集的EEG信号转换为可听声音，将电压振幅转化为音频频率和强度。
• 应用多分形去趋势互相关分析（MFDXA）量化音色化EEG信号与塔普拉音频信号之间的互相关性。
• 分析聚焦于不同脑叶，计算互相关系数γx以评估跨尺度和区域的相互依赖性。
• 比较静息状态与音乐暴露期间EEG与音乐刺激的多分形互相关行为，以检测脑活动的动态变化。
• 对各脑叶γx值的统计分析揭示了在不同刺激下神经电活动响应模式的差异。

实验结果

研究问题

• RQ1 EEG信号能否被有效音色化以揭示可感知的大脑活动模式？
• RQ2 EEG与音乐刺激（塔普拉持续音）之间的互相关性在不同脑区如何变化？
• RQ3 与非音乐刺激相比，音乐刺激是否引发更广泛、更分散的大脑激活？
• RQ4 多分形动力学在检测脑信号与外部音乐之间细微相关性方面发挥什么作用？
• RQ5 在静息与听音乐期间，γx值在不同脑叶如何变化，从而指示神经生理参与程度？

主要发现

• MFDXA分析揭示了音色化EEG信号与塔普拉持续音之间存在显著且持续的互相关性，表明存在强烈的神经-声学耦合。
• 音乐刺激独特地激活了多个脑叶，而非音乐刺激仅激活特定、局部的区域。
• 在音乐暴露期间，γx值在不同脑叶间表现出有意义的差异，表明对听觉刺激存在区域特异性反应。
• 音色化过程成功地将原始EEG数据转化为可感知的音频，实现了对神经动力学的实时听觉解读。
• 本研究首次通过MFDXA展示了自然音乐刺激与多分形EEG动力学之间的直接、可量化的相关性。
• 结果表明，音乐具有独特能力，可同步并激活大脑中广泛分布的神经网络。