The neural activation patterns provoked in response to music listening can reveal whether a subject did or did not receive music training. In the current exploratory study, we have approached this two-group (musicians and nonmusicians) classification problem through a computational framework composed of the following steps: Acoustic features extraction; Acoustic features selection; Trigger selection; EEG signal processing; and Multivariate statistical analysis. We are particularly interested in analyzing the brain data on a global level, considering its activity registered in electroencephalogram (EEG) signals on a given time instant. Our experiment9s results—with 26 volunteers (13 musicians and 13 nonmusicians) who listened the classical music Hungarian Dance No. 5 from Johannes Brahms—have shown that is possible to linearly differentiate musicians and nonmusicians with classification accuracies that range from 69.2% (test set) to 93.8% (training set), despite the limited sample sizes available. Additionally, given the whole brain vector navigation method described and implemented here, our results suggest that it is possible to highlight the most expressive and discriminant changes in the participants brain activity patterns depending on the acoustic feature extracted from the audio.

中文翻译：

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全脑性脑电图分析

响应音乐聆听而激发的神经激活模式可以揭示受试者是否接受了音乐训练。在当前的探索性研究中，我们已经通过包含以下步骤的计算框架来解决了这两个类别（音乐家和非音乐家）的分类问题：声学特征提取；声学特征选择；触发选择；脑电信号处理；和多元统计分析。考虑到在给定时刻脑电图（EEG）信号中记录的大脑活动，我们对在全球范围内分析大脑数据特别感兴趣。我们的实验结果是-有26位志愿者（13位音乐家和13位非音乐家）听了古典音乐匈牙利舞蹈No. Johannes Brahms的第5张文章显示，尽管可用样本量有限，但分类精度在69.2％（测试集）至93.8％（训练集）之间的线性精度可以区分音乐家和非音乐家。此外，鉴于此处描述和实施的全脑矢量导航方法，我们的结果表明，有可能根据从音频中提取的声学特征，突出参与者大脑活动模式中最具表现力和判别力的变化。