Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals
Lately, the research on human emotion has attracted the interest of several disciplines,including computer science, cognitive science, and psychology. As such, the aim of studywas to examine the effects of binaural beats music on depression disorders. This study wasconducted based on an experimental...
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RC Internal medicine Mohammed Hamada Jasim Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals |
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Lately, the research on human emotion has attracted the interest of several disciplines,including computer science, cognitive science, and psychology. As such, the aim of studywas to examine the effects of binaural beats music on depression disorders. This study wasconducted based on an experimental design in which electroencephalography (EEG) wasutilized to capture brain signals. The EEGlab toolbox of Matlab was used to extract therelevant features of the brain signals. For feature filtering, brain signals were filtered byusing Butterworth 5th order. EEG signals were then converted from the time to thefrequency domain by utilizing Fast Fourier Transform (FFT). A sample of 90 depressiveparticipants was exposed to binaural beats music. One-way ANOVA was used to comparethe differences in the effects based on three different time intervals, which were labelled asbefore listening, during listening, and after listening phases. Descriptive and statisticalanalysis were utilized to analyse the effects of binaural beat music on the subjectsdepression level and to examine whether there were significant differences among theintervals. The findings showed that 63.2% of the subjects exhibited positive responsesbased on either an increasing relaxation level or a decreasing depression level or both, withthe remaining subjects exhibiting negative responses. In addition, the most conductiveelectrodes were found to be the F3, F7 electrodes, which effectively captured alpha andbeta bands from the frontal lobe area of the brain. Furthermore, the one-way ANOVAresults indicated that there were no significant differences in the effects among the intervals[F (2, 87) =1, 86, p = 0.161]. Overall, this study highlights the benefits of the use of binauralbeats music in the level of depression and to improve the relaxation state of those sufferingfrom depression disorders. For future research, examining the effects of binaural beat musicon other aspects of human emotions is recommended. |
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Master's degree |
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Mohammed Hamada Jasim |
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Mohammed Hamada Jasim |
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Mohammed Hamada Jasim |
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Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals |
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Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals |
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Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals |
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Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals |
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Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals |
| title_sort |
exploring the impacts of listening to binaural beats music on non-medical depression disorders by using eeg signals |
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Universiti Pendidikan Sultan Idris |
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Fakulti Seni, Komputeran dan Industri Kreatif |
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2019 |
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oai:ir.upsi.edu.my:63852021-10-28 Exploring the impacts of listening to binaural beats music on non-medical depression disorders by using EEG signals 2019 Mohammed Hamada Jasim RC Internal medicine Lately, the research on human emotion has attracted the interest of several disciplines,including computer science, cognitive science, and psychology. As such, the aim of studywas to examine the effects of binaural beats music on depression disorders. This study wasconducted based on an experimental design in which electroencephalography (EEG) wasutilized to capture brain signals. The EEGlab toolbox of Matlab was used to extract therelevant features of the brain signals. For feature filtering, brain signals were filtered byusing Butterworth 5th order. EEG signals were then converted from the time to thefrequency domain by utilizing Fast Fourier Transform (FFT). A sample of 90 depressiveparticipants was exposed to binaural beats music. One-way ANOVA was used to comparethe differences in the effects based on three different time intervals, which were labelled asbefore listening, during listening, and after listening phases. Descriptive and statisticalanalysis were utilized to analyse the effects of binaural beat music on the subjectsdepression level and to examine whether there were significant differences among theintervals. The findings showed that 63.2% of the subjects exhibited positive responsesbased on either an increasing relaxation level or a decreasing depression level or both, withthe remaining subjects exhibiting negative responses. In addition, the most conductiveelectrodes were found to be the F3, F7 electrodes, which effectively captured alpha andbeta bands from the frontal lobe area of the brain. Furthermore, the one-way ANOVAresults indicated that there were no significant differences in the effects among the intervals[F (2, 87) =1, 86, p = 0.161]. Overall, this study highlights the benefits of the use of binauralbeats music in the level of depression and to improve the relaxation state of those sufferingfrom depression disorders. For future research, examining the effects of binaural beat musicon other aspects of human emotions is recommended. 2019 thesis https://ir.upsi.edu.my/detailsg.php?det=6385 https://ir.upsi.edu.my/detailsg.php?det=6385 text eng closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Adamos, D. A., Dimitriadis, S. I., & Laskaris, N. A. (2016). Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference. Information Sciences, 343344, 94108.https://doi.org/10.1016/j.ins.2016.01.005Akdemir Akar, S., Kara, S., Agambayev, S., & Bilgi??, V. (2015). Nonlinear analysis of EEGs ofpatients with major depression during different emotional states. 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