A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis
Electroencephalography (EEG) analysis is critical for diagnosing various neurological disorders and for other brain-related control applications. However, the presence of artifacts, including muscle artifacts, significantly alter signal power and lead to misinterpretation of neural information. Henc...
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2025.2514941 |
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| author | Vandana Akshath Raj Subramanya G. Nayak Ananthakrishna Thalengala |
| author_facet | Vandana Akshath Raj Subramanya G. Nayak Ananthakrishna Thalengala |
| author_sort | Vandana Akshath Raj |
| collection | DOAJ |
| description | Electroencephalography (EEG) analysis is critical for diagnosing various neurological disorders and for other brain-related control applications. However, the presence of artifacts, including muscle artifacts, significantly alter signal power and lead to misinterpretation of neural information. Hence, it is essential to identify and remove these signals to enable a reliable EEG interpretation. By addressing the limitations of existing signal decomposition methods, this study develops a hybrid approach combining variational mode decomposition (VMD), stationary wavelet transform (SWT), and canonical correlation analysis (CCA) to enhance EEG signal quality by precisely aiming to remove muscle artifacts. The study utilized EEGdenoiseNet, an open-source dataset, to test the efficacy of the proposed method. The performance metrics employed in this study include: signal-to-noise ratio (SNR), correlation coefficient (CC), root mean square error (RMSE), and power spectral density (PSD). The findings indicate that the proposed approach yields superior results, with an average SNR improvement of 4.1360 dB and an average CC of 0.8171 compared with the combination of the VMD-CCA, EMD-CCA, EEMD-CCA, and EWT methods. The lower RMSE values and PSD plots further demonstrate the effectiveness of the method in muscle artifact suppression while retaining the relevant EEG characteristics. |
| format | Article |
| id | doaj-art-ffe07ec29a8d4cf689c8ffb839b3633a |
| institution | Kabale University |
| issn | 2331-1916 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj-art-ffe07ec29a8d4cf689c8ffb839b3633a2025-08-20T03:26:39ZengTaylor & Francis GroupCogent Engineering2331-19162025-12-0112110.1080/23311916.2025.2514941A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysisVandana Akshath Raj0Subramanya G. Nayak1Ananthakrishna Thalengala2Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaElectroencephalography (EEG) analysis is critical for diagnosing various neurological disorders and for other brain-related control applications. However, the presence of artifacts, including muscle artifacts, significantly alter signal power and lead to misinterpretation of neural information. Hence, it is essential to identify and remove these signals to enable a reliable EEG interpretation. By addressing the limitations of existing signal decomposition methods, this study develops a hybrid approach combining variational mode decomposition (VMD), stationary wavelet transform (SWT), and canonical correlation analysis (CCA) to enhance EEG signal quality by precisely aiming to remove muscle artifacts. The study utilized EEGdenoiseNet, an open-source dataset, to test the efficacy of the proposed method. The performance metrics employed in this study include: signal-to-noise ratio (SNR), correlation coefficient (CC), root mean square error (RMSE), and power spectral density (PSD). The findings indicate that the proposed approach yields superior results, with an average SNR improvement of 4.1360 dB and an average CC of 0.8171 compared with the combination of the VMD-CCA, EMD-CCA, EEMD-CCA, and EWT methods. The lower RMSE values and PSD plots further demonstrate the effectiveness of the method in muscle artifact suppression while retaining the relevant EEG characteristics.https://www.tandfonline.com/doi/10.1080/23311916.2025.2514941Canonical correlation analysiselectroencephalogrammuscle artifactspower spectral densitysignal-to-noise ratiostationary wavelet transform |
| spellingShingle | Vandana Akshath Raj Subramanya G. Nayak Ananthakrishna Thalengala A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis Cogent Engineering Canonical correlation analysis electroencephalogram muscle artifacts power spectral density signal-to-noise ratio stationary wavelet transform |
| title | A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis |
| title_full | A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis |
| title_fullStr | A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis |
| title_full_unstemmed | A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis |
| title_short | A hybrid framework for muscle artifact removal in EEG: combining variational mode decomposition, stationary wavelet transform, and canonical correlation analysis |
| title_sort | hybrid framework for muscle artifact removal in eeg combining variational mode decomposition stationary wavelet transform and canonical correlation analysis |
| topic | Canonical correlation analysis electroencephalogram muscle artifacts power spectral density signal-to-noise ratio stationary wavelet transform |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2025.2514941 |
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