Cargando…
Decoding intracranial EEG data with multiple kernel learning method
BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their application to electrophysiological data has been les...
Autores principales: | Schrouff, Jessica, Mourão-Miranda, Janaina, Phillips, Christophe, Parvizi, Josef |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier/North-Holland Biomedical Press
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758824/ https://www.ncbi.nlm.nih.gov/pubmed/26692030 http://dx.doi.org/10.1016/j.jneumeth.2015.11.028 |
Ejemplares similares
-
Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models
por: Schrouff, Jessica, et al.
Publicado: (2018) -
Fast temporal dynamics and causal relevance of face processing in the human temporal cortex
por: Schrouff, Jessica, et al.
Publicado: (2020) -
Decoding Intracranial EEG With Machine Learning: A Systematic Review
por: Mirchi, Nykan, et al.
Publicado: (2022) -
Decoding Sequence Learning from Single-Trial Intracranial EEG in Humans
por: De Lucia, Marzia, et al.
Publicado: (2011) -
Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes
por: Schrouff, Jessica, et al.
Publicado: (2012)