Cargando…
Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering
Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mas...
Autores principales: | Lin, Chin-Teng, Huang, Chih-Sheng, Yang, Wen-Yu, Singh, Avinash Kumar, Chuang, Chun-Hsiang, Wang, Yu-Kai |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823426/ https://www.ncbi.nlm.nih.gov/pubmed/29599950 http://dx.doi.org/10.1155/2018/5081258 |
Ejemplares similares
-
Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal
por: Roy, Vandana, et al.
Publicado: (2017) -
GMMchi: gene expression clustering using Gaussian mixture modeling
por: Liu, Ta-Chun, et al.
Publicado: (2022) -
Avoiding inferior clusterings with misspecified Gaussian mixture models
por: Kasa, Siva Rajesh, et al.
Publicado: (2023) -
Identifying changes in EEG information transfer during drowsy driving by transfer entropy
por: Huang, Chih-Sheng, et al.
Publicado: (2015) -
A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency
por: Ye, Xulun, et al.
Publicado: (2018)