WAVELET TRANSFORMS FOR EEG SIGNAL DENOISING AND DECOMPOSITION
EEG signal analysis is difficult because there are so many unwanted impulses from non-cerebral sources. Presently, methods for eliminating noise through selective frequency filtering are afflicted with a notable deprivation of EEG information. Therefore, even if the noise is decreased, the signal...
Main Authors: | Ibtihal Hassan Elshekhidris, Magdi Baker MohamedAmien, Ahmed Fragoon |
---|---|
Format: | Article |
Language: | English |
Published: |
XLESCIENCE
2023-12-01
|
Series: | International Journal of Advances in Signal and Image Sciences |
Subjects: | |
Online Access: | https://xlescience.org/index.php/IJASIS/article/view/130 |
Similar Items
-
Wavelet transform-based mode decomposition for EEG signals under general anesthesia
by: Shoko Yamochi, et al.
Published: (2024-11-01) -
Automated classification of epileptic seizures using modified one-dimensional convolution neural network based on empirical mode decomposition with high accuracy
by: Ibtihal Hassan Elshekhidris, et al.
Published: (2025-06-01) -
Multimode Decomposition and Wavelet Threshold Denoising of Mold Level Based on Mutual Information Entropy
by: Zhufeng Lei, et al.
Published: (2019-02-01) -
An effective electrocardiogram segments denoising method combined with ensemble empirical mode decomposition, empirical mode decomposition, and wavelet packet
by: Yaru Yue, et al.
Published: (2023-06-01) -
Denoising of Raman Spectra Using a Neural Network Based on Variational Mode Decomposition, Empirical Wavelet Transform, and Encoder-Bidirectional Long Short-Term Memory
by: Xuyi Zhang, et al.
Published: (2023-11-01)