脑音乐与脑音乐：一种新的脑电可听化方法

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

摘要翻译：
我们能听到大脑的声音吗？有没有什么技术可以让我们听到来自大脑不同叶的神经电脉冲？所有这些问题的答案都是肯定的。本文提出了一种新的方法，可以对静止状态下以及在一种最简单的声学刺激----tanpura Drone----的影响下记录的脑电图(EEG)数据进行超声处理。tanpura无人机有一个非常简单但非常复杂的声学特征，通常用于在音乐表演中创造环境。因此，在这个试点项目中，我们选择研究简单的声学刺激（tanpura drone）和超声EEG数据之间的相关性。到目前为止，还没有关于生物信号与声学信号之间的直接相关性，以及这种相关性在不同类型刺激的影响下如何变化的研究。这是首次利用多重分形去散互相关分析(MFDXA)的健壮数学显微镜来弥补这一鸿沟，并寻找音乐信号和脑电数据之间的直接相关性。为此，我们在2分钟的“休息状态”（即有白噪声）和2分钟的“Tanpura drone”（音乐刺激）听力条件下，对10名参与者进行了脑电图测试。然后对来自不同电极的脑电信号进行超声处理，用MFDXA技术评估tanpura信号与脑电信号的相关程度（或互相关系数）。在实验过程中，不同叶的{γ}x的变化也提供了重要的有趣的新信息。只有音乐刺激才有能力接触大脑的几个区域，与其他刺激不同（只接触特定的领域）。
---
英文标题：
《Music of Brain and Music on Brain: A Novel EEG Sonification approach》
---
作者：
Sayan Nag, Shankha Sanyal, Archi Banerjee, Ranjan Sengupta and Dipak
  Ghosh
---
最新提交年份：
2017
---
分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
--
一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
--
一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
--
一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
--

---
英文摘要：
  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 {\gamma}x 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).
---
PDF链接：
https://arxiv.org/pdf/1712.08336

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：Mathematical Modification Applications Architecture cancellation different 答案 大脑 相关性 状态