Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing
Electroencephalogram (EEG) is a non-invasive technology that is widely used to record the electrical activity of the brain. However, often the EEG signal is contaminated by noise, including ocular artefacts and muscle activity, which can interfere with accurate analysis and interpretation. This rese...
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2025-01-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/110488 |
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| author | I Putu Agus Eka Darma Udayana Made Sudarma I Ketut Gede Darma Putra I Made Sukarsa Minho Jo |
| author_facet | I Putu Agus Eka Darma Udayana Made Sudarma I Ketut Gede Darma Putra I Made Sukarsa Minho Jo |
| author_sort | I Putu Agus Eka Darma Udayana |
| collection | DOAJ |
| description | Electroencephalogram (EEG) is a non-invasive technology that is widely used to record the electrical activity of the brain. However, often the EEG signal is contaminated by noise, including ocular artefacts and muscle activity, which can interfere with accurate analysis and interpretation. This research aims to improve the quality of EEG signals related to concentration by comparing the effectiveness of two denoising methods, namely Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Using commercial EEG headsets, this study recorded Alpha, Beta, Delta, and Theta signals from 20 participants while they performed tasks that required concentration. Evaluation of the effectiveness of the denoising technique is carried out by focusing on changes in standard deviation and calculating the Percentage Residual Difference (PRD) value of the EEG signal before and after denoising. The results show that ICA provides better denoising performance than PCA, as reflected by a significant reduction in standard deviation and a lower PRD value. These results indicate that the ICA method can effectively reduce noise and preserve important information from the original signal. |
| format | Article |
| id | doaj-art-012bf8b6038f4f63a7157055115cc14e |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-012bf8b6038f4f63a7157055115cc14e2025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322025-01-01150212413310.24843/LKJITI.2024.v15.i02.p05110488Comparative Analysis of Denoising Techniques for Optimizing EEG Signal ProcessingI Putu Agus Eka Darma Udayana0Made Sudarma1I Ketut Gede Darma Putra2I Made Sukarsa3Minho Jo4Indonesian Institute of Business and TechnologyUdayana UniversityUdayana UniversityUdayana UniversityDepartment Of Computer Convergence Software, Korea UniversityElectroencephalogram (EEG) is a non-invasive technology that is widely used to record the electrical activity of the brain. However, often the EEG signal is contaminated by noise, including ocular artefacts and muscle activity, which can interfere with accurate analysis and interpretation. This research aims to improve the quality of EEG signals related to concentration by comparing the effectiveness of two denoising methods, namely Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Using commercial EEG headsets, this study recorded Alpha, Beta, Delta, and Theta signals from 20 participants while they performed tasks that required concentration. Evaluation of the effectiveness of the denoising technique is carried out by focusing on changes in standard deviation and calculating the Percentage Residual Difference (PRD) value of the EEG signal before and after denoising. The results show that ICA provides better denoising performance than PCA, as reflected by a significant reduction in standard deviation and a lower PRD value. These results indicate that the ICA method can effectively reduce noise and preserve important information from the original signal.https://ojs.unud.ac.id/index.php/lontar/article/view/110488 |
| spellingShingle | I Putu Agus Eka Darma Udayana Made Sudarma I Ketut Gede Darma Putra I Made Sukarsa Minho Jo Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing Lontar Komputer |
| title | Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing |
| title_full | Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing |
| title_fullStr | Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing |
| title_full_unstemmed | Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing |
| title_short | Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing |
| title_sort | comparative analysis of denoising techniques for optimizing eeg signal processing |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/110488 |
| work_keys_str_mv | AT iputuagusekadarmaudayana comparativeanalysisofdenoisingtechniquesforoptimizingeegsignalprocessing AT madesudarma comparativeanalysisofdenoisingtechniquesforoptimizingeegsignalprocessing AT iketutgededarmaputra comparativeanalysisofdenoisingtechniquesforoptimizingeegsignalprocessing AT imadesukarsa comparativeanalysisofdenoisingtechniquesforoptimizingeegsignalprocessing AT minhojo comparativeanalysisofdenoisingtechniquesforoptimizingeegsignalprocessing |