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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2088-1541 2541-5832 |