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Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique

Depression can be detected through screening tests and non-invasive examinations at specific clinics, yet a professional must verify the severity. If mild depressions are not detected, it can lead to major depressions. Eventually, this could also be fatal. Collectively, the studies from the literatu...

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Main Authors: Thulasi, K., Balakrishnan, Sumathi, Yap, Jia Suan, Yan, Xiao Qing, Malarvili, M. B., Murugesan, R. K., Devandran, Pagupathi
Format: Conference or Workshop Item
Published: 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/107700/
http://dx.doi.org/10.1109/NBEC58134.2023.10352586
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spelling my.utm.1077002024-10-02T06:29:56Z http://eprints.utm.my/107700/ Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique Thulasi, K. Balakrishnan, Sumathi Yap, Jia Suan Yan, Xiao Qing Malarvili, M. B. Murugesan, R. K. Devandran, Pagupathi TK Electrical engineering. Electronics Nuclear engineering Depression can be detected through screening tests and non-invasive examinations at specific clinics, yet a professional must verify the severity. If mild depressions are not detected, it can lead to major depressions. Eventually, this could also be fatal. Collectively, the studies from the literature review outline the critical role of EEG in revolutionising the recognition of depression disability through machine learning prediction models. This project aims to find a solution that enables the acquisition of EEG signals using commercially available headsets, apply machine learning algorithms for processing, and determine mild depression detection. The system's accuracy is tested to ensure it reaches a safe percentage of precision. Additionally, the paper addresses open issues encountered during the project. 2023 Conference or Workshop Item PeerReviewed Thulasi, K. and Balakrishnan, Sumathi and Yap, Jia Suan and Yan, Xiao Qing and Malarvili, M. B. and Murugesan, R. K. and Devandran, Pagupathi (2023) Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique. In: 2023 IEEE 2nd National Biomedical Engineering Conference (NBEC), 5 September 2023-7 September 2023, Melaka, Malaysia. http://dx.doi.org/10.1109/NBEC58134.2023.10352586
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Thulasi, K.
Balakrishnan, Sumathi
Yap, Jia Suan
Yan, Xiao Qing
Malarvili, M. B.
Murugesan, R. K.
Devandran, Pagupathi
Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique
description Depression can be detected through screening tests and non-invasive examinations at specific clinics, yet a professional must verify the severity. If mild depressions are not detected, it can lead to major depressions. Eventually, this could also be fatal. Collectively, the studies from the literature review outline the critical role of EEG in revolutionising the recognition of depression disability through machine learning prediction models. This project aims to find a solution that enables the acquisition of EEG signals using commercially available headsets, apply machine learning algorithms for processing, and determine mild depression detection. The system's accuracy is tested to ensure it reaches a safe percentage of precision. Additionally, the paper addresses open issues encountered during the project.
format Conference or Workshop Item
author Thulasi, K.
Balakrishnan, Sumathi
Yap, Jia Suan
Yan, Xiao Qing
Malarvili, M. B.
Murugesan, R. K.
Devandran, Pagupathi
author_facet Thulasi, K.
Balakrishnan, Sumathi
Yap, Jia Suan
Yan, Xiao Qing
Malarvili, M. B.
Murugesan, R. K.
Devandran, Pagupathi
author_sort Thulasi, K.
title Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique
title_short Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique
title_full Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique
title_fullStr Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique
title_full_unstemmed Detecting mild depression from EEG signal in a non-clinical environment using machine learning technique
title_sort detecting mild depression from eeg signal in a non-clinical environment using machine learning technique
publishDate 2023
url http://eprints.utm.my/107700/
http://dx.doi.org/10.1109/NBEC58134.2023.10352586
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score 13.211869

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