Human stress classification using EEG signals in response to music tracks
Human stress classification using EEG signals in response to music tracks
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DOI：
10.1016/j.compbiomed.2019.02.015
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发表时间：
2019-04-01
影响因子：
7.7
通讯作者：
Anwar, Syed Muhammad
中科院分区：
文献类型：
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作者：
Asif, Anum;Majid, Muhammad;Anwar, Syed Muhammad
几乎每个人在人生的某个阶段都会不可避免地感受到压力。可靠而准确的压力测量可以给出一个人的压力负担的估计。为了更好的健康，有必要采取必要的步骤来减轻负担和重新控制。听音乐是帮助打破压力的一种方式。这项研究利用大脑信号研究了英语和乌尔都语音乐对人类压力水平的影响。27名受试者自愿参加了这项研究，其中14名男性和13名女性以乌尔都语为第一语言，年龄从20岁到35岁不等。参与者的脑电图仪(EEG)信号被记录下来，同时使用四通道缪斯发带听不同的音乐。参与者被要求使用状态和特质焦虑问卷主观地报告他们的压力水平。本研究使用的英语音乐被分为四种类型，即摇滚乐、金属乐、电子乐和说唱。乌尔都语音乐曲目由五种流派组成，即著名的、爱国的、悠扬的、卡瓦利和加扎尔。从四个通道五个频段的经预处理的脑电信号中提取了绝对功率、相对功率、相干性、相位滞后和幅度不对称性五组特征，并将其用于应力分类。使用序列最小优化、随机下降梯度、Logistic回归和多层感知器四种分类算法将受试者的压力水平分为两类和三类。结果表明，LR对应力的识别效果较好，最高分类正确率分别为98.76%和95.06%。为了理解压力中与性别、语言和体裁相关的歧视，我们使用了t检验和单向方差分析。结果表明，与乌尔都语音乐相比，英语音乐对缓解压力有更大的影响。在两种语言的体裁中，没有发现明显的差异。此外，女性报告的分数与男性相比存在显著差异。这表明，与男性相比，女性的应激行为对音乐更敏感。
Stress is inevitably experienced by almost every person at some stage of their life. A reliable and accurate measurement of stress can give an estimate of an individual's stress burden. It is necessary to take essential steps to relieve the burden and regain control for better health. Listening to music is a way that can help in breaking the hold of stress. This study examines the effect of music tracks in English and Urdu language on human stress level using brain signals. Twenty-seven subjects including 14 males and 13 females having Urdu as their first language, with ages ranging from 20 to 35 years, voluntarily participated in the study. The electroencephalograph (EEG) signals of the participants are recorded, while listening to different music tracks by using a four-channel MUSE headband. Participants are asked to subjectively report their stress level using the state and trait anxiety questionnaire. The English music tracks used in this study are categorized into four genres i.e., rock, metal, electronic, and rap. The Urdu music tracks consist of five genres i.e., famous, patriotic, melodious, qawali, and ghazal. Five groups of features including absolute power, relative power, coherence, phase lag, and amplitude asymmetry are extracted from the preprocessed EEG signals of four channels and five bands, which are used by the classifier for stress classification. Four classifier algorithms namely sequential minimal optimization, stochastic decent gradient, logistic regression (LR), and multilayer perceptron are used to classify the subject's stress level into two and three classes. It is observed that LR performs well in identifying stress with the highest reported accuracy of 98.76% and 95.06% for two- and three-level classification respectively. For understanding gender, language, and genre related discriminations in stress, a t-test and one-way analysis of variance is used. It is evident from results that English music tracks have more influence on stress level reduction as compared to Urdu music tracks. Among the genres of both languages, a noticeable difference is not found. Moreover, significant difference is found in the scores reported by females as compared to males. This indicates that the stress behavior of females is more sensitive to music as compared to males.