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Optimization Driven Variational Autoencoder GAN for Artifact Reduction in EEG Signals for Improved Neurological Disorder and Disability Assessment

Mohamed Yacin Sikkandar

Mohamed Yacin Sikkandar

Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah UniversityAl Majmaah, Saudi Arabia
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Sikkandar, Mohamed Yacin
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S. Sabarunisha Begum

S. Sabarunisha Begum

Department of Biotechnology, P.S.R. Engineering CollegeSivakasi, India
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Sabarunisha Begum, S.
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Ahmad Alassaf

Ahmad Alassaf

Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah UniversityAl Majmaah, Saudi Arabia
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Alassaf, Ahmad
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Ibrahim AlMohimeed

Ibrahim AlMohimeed

Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah UniversityAl Majmaah, Saudi Arabia
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AlMohimeed, Ibrahim
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Khalid Alhussaini

Khalid Alhussaini

Department of Biomedical Technology, College of Applied Medical Sciences, King Saud UniversityRiyadh, Saudi Arabia
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Alhussaini, Khalid
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Adham Aleid

Adham Aleid

Department of Biomedical Technology, College of Applied Medical Sciences, King Saud UniversityRiyadh, Saudi Arabia
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Aleid, Adham
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Abdulrahman Khalid Alhaidar

Abdulrahman Khalid Alhaidar

Support Services Department, Tumair General Hospital, Ministry of HealthTumair, Saudi Arabia
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Alhaidar, Abdulrahman Khalid
  
24 lut 2025
Optimization Driven Variational Autoencoder GAN for Artifact Reduction in EEG Signals for Improved Neurological Disorder and Disability Assessment's Cover Image
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Data publikacji: 24 lut 2025
Zakres stron: 10 - 14
Otrzymano: 04 cze 2024
Przyjęty: 16 sty 2025
DOI: https://doi.org/10.2478/msr-2025-0002
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© 2025 Mohamed Yacin Sikkandar et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

BSO-VAE-GAN architecture for artifact reduction.
BSO-VAE-GAN architecture for artifact reduction.

Fig. 2.

Comparison of MSE with the EEG+brain signal artifact.
Comparison of MSE with the EEG+brain signal artifact.

Fig. 3.

Comparison of MSE with the EEG+eye signal artifact.
Comparison of MSE with the EEG+eye signal artifact.

Fig. 4.

Comparison of MSE with the EEG+muscle signal artifact.
Comparison of MSE with the EEG+muscle signal artifact.

Accuracy performance of the proposed BrOpt_VAGAN model_

Mixtures of artifact components Accuracy [%] Error [%]
Pseudo-clean brain 98.5 12.41
eye 96.2 11.53
muscle 97.3 12.74
Noisy input brain 98.6 11.84
eye 95.9 11.90
muscle 93.5 12.56
Język:
Angielski
Częstotliwość wydawania:
6 razy w roku
Dziedziny czasopisma:
Inżynieria, Elektrotechnika, Inżynieria sterowania, metrologia i testowanie
Kanał RSS czasopisma