MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural n...

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Detalles Bibliográficos
Autores principales: Hossain, Md Shafayet, Mahmud, Sakib, Khandakar, Amith, Al-Emadi, Nasser, Chowdhury, Farhana Ahmed, Mahbub, Zaid Bin, Reaz, Mamun Bin Ibne, Chowdhury, Muhammad E. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215884/
https://www.ncbi.nlm.nih.gov/pubmed/37237649
http://dx.doi.org/10.3390/bioengineering10050579

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215884/
https://www.ncbi.nlm.nih.gov/pubmed/37237649
http://dx.doi.org/10.3390/bioengineering10050579

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