Literature DB >> 30836290

Human stress classification using EEG signals in response to music tracks.

Anum Asif1, Muhammad Majid2, Syed Muhammad Anwar3.   

Abstract

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.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Classification; Electroencephalography (EEG); Feature extraction; Human stress; Music; State trait anxiety inventory (STAI)

Mesh:

Year:  2019        PMID: 30836290     DOI: 10.1016/j.compbiomed.2019.02.015

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  14 in total

1.  Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring.

Authors:  Eyad Talal Attar; Vignesh Balasubramanian; Ersoy Subasi; Mehmet Kaya
Journal:  IEEE J Transl Eng Health Med       Date:  2021-08-23       Impact factor: 3.316

2.  Virtual Reality Customized 360-Degree Experiences for Stress Relief.

Authors:  Miguel A Vaquero-Blasco; Eduardo Perez-Valero; Christian Morillas; Miguel A Lopez-Gordo
Journal:  Sensors (Basel)       Date:  2021-03-22       Impact factor: 3.576

3.  Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings.

Authors:  Yang Chang; Congying He; Bo-Yu Tsai; Li-Wei Ko
Journal:  Front Hum Neurosci       Date:  2021-12-22       Impact factor: 3.169

4.  Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm.

Authors:  Ala Hag; Dini Handayani; Maryam Altalhi; Thulasyammal Pillai; Teddy Mantoro; Mun Hou Kit; Fares Al-Shargie
Journal:  Sensors (Basel)       Date:  2021-12-15       Impact factor: 3.576

5.  Exploring anxiety awareness during academic science examinations.

Authors:  Hippokratis Apostolidis; Thrasyvoulos Tsiatsos
Journal:  PLoS One       Date:  2021-12-15       Impact factor: 3.240

6.  A Music Emotion Classification Model Based on the Improved Convolutional Neural Network.

Authors:  Xiaosong Jia
Journal:  Comput Intell Neurosci       Date:  2022-02-14

7.  Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session.

Authors:  Eduardo Perez-Valero; Miguel A Vaquero-Blasco; Miguel A Lopez-Gordo; Christian Morillas
Journal:  Front Comput Neurosci       Date:  2021-07-14       Impact factor: 2.380

8.  An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes.

Authors:  Omneya Attallah
Journal:  Diagnostics (Basel)       Date:  2020-05-09

9.  EEG based Classification of Long-term Stress Using Psychological Labeling.

Authors:  Sanay Muhammad Umar Saeed; Syed Muhammad Anwar; Humaira Khalid; Muhammad Majid; And Ulas Bagci
Journal:  Sensors (Basel)       Date:  2020-03-29       Impact factor: 3.576

10.  Modified Support Vector Machine for Detecting Stress Level Using EEG Signals.

Authors:  Richa Gupta; M Afshar Alam; Parul Agarwal
Journal:  Comput Intell Neurosci       Date:  2020-08-01
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.