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首页> 外文期刊>Computers in Biology and Medicine >Human stress classification using EEG signals in response to music tracks
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Human stress classification using EEG signals in response to music tracks

机译:使用EEG信号进行响应音乐轨道的人力压力分类

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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.
机译:几乎每个人都在他们生命的某些阶段不可避免地经历过压力。可靠和准确的压力测量可以估计个人的压力负担。有必要采取必要的步骤来缓解负担并重新获得控制以获得更好的健康。听音乐是一种可以帮助打破压力的一种方式。本研究审查了音乐曲目在使用脑信号对人力压力水平对英语和乌尔都语的影响。二十七名受试者,包括14名男性和13名女性,其中乌尔都语是他们的第一语言,年龄在20至35年的年龄,自愿参加了该研究。记录参与者的脑电图(EEG)信号,同时通过使用四通道MUSE头带收听不同的音乐轨道。要求参与者使用国家和特质焦虑调查问卷主观地报告他们的压力水平。本研究中使用的英语音乐轨道分为四种类型,即岩石,金属,电子和说唱。乌尔都语音乐曲目由五个类型的曲目包括,着名,爱国,丝带,Qawali和Ghazal。从四个通道的预处理EEG信号和五个频带中提取包括绝对功率,相对功率,相干性,相滞和幅度不对称的五组特征,其由分类器用于应力分类。四分类器算法即顺序最小优化,随机体面梯度,逻辑回归(LR)和多层Perceptron用于将受试者的应力水平分类为两个和三个类别。观察到,LR分别在识别最高报告的准确度为98.76%和95.06%,分别对两级和三级分类的胁迫进行良好。为了了解压力的性别,语言和类型相关的鉴别,使用T检验和单向性方差分析。与Urdu音乐轨道相比,英语音乐轨道对压力水平降低的影响很明显。在两种语言的类型中,找不到明显的差异。此外,与男性相比,女性报告的分数中发现了显着差异。这表明女性的应力行为与男性相比,女性对音乐更敏感。

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