Cargando…
Hybrid ICA—Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals
Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results i...
Autores principales: | Mannan, Malik M. Naeem, Jeong, Myung Y., Kamran, Muhammad A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4853904/ https://www.ncbi.nlm.nih.gov/pubmed/27199714 http://dx.doi.org/10.3389/fnhum.2016.00193 |
Ejemplares similares
-
Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal
por: Mannan, Malik M. Naeem, et al.
Publicado: (2016) -
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
por: Winkler, Irene, et al.
Publicado: (2011) -
MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks
por: Treacher, Alex H., et al.
Publicado: (2021) -
A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings
por: Tamburro, Gabriella, et al.
Publicado: (2018) -
Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks
por: Al-Qazzaz, Noor Kamal, et al.
Publicado: (2017)