Cargando…
Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet tra...
Autores principales: | Wang, Xian-Yu, Li, Cong, Zhang, Rui, Wang, Liang, Tan, Jin-Lin, Wang, Hai |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198366/ https://www.ncbi.nlm.nih.gov/pubmed/35720691 http://dx.doi.org/10.3389/fnins.2022.921642 |
Ejemplares similares
-
Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings
por: Yang, Zijing, et al.
Publicado: (2012) -
Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization
por: Li, Siyu, et al.
Publicado: (2021) -
Potential of Overcomplete Wavelet Frame Expansion for Facilitating Electroencephalogram Information Mining
por: Liu, Wanshan, et al.
Publicado: (2022) -
Ripples in mathematics: the discrete wavelet transform
por: Jensen, Arne, et al.
Publicado: (2001) -
Feature Extraction of 3T3 Fibroblast Microtubule Based on Discrete Wavelet Transform and Lucy–Richardson Deconvolution Methods
por: Bai, Haoxin, et al.
Publicado: (2022)