Cargando…
A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification
In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not suffic...
Autores principales: | Mutlu, Ali Yener, Bernat, Edward, Aviyente, Selin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427740/ https://www.ncbi.nlm.nih.gov/pubmed/22934122 http://dx.doi.org/10.1155/2012/451516 |
Ejemplares similares
-
Graph-to-signal transformation based classification of functional connectivity brain networks
por: Munia, Tamanna Tabassum Khan, et al.
Publicado: (2019) -
A practical introduction to EEG Time-Frequency Principal Components Analysis (TF-PCA)
por: Buzzell, George A., et al.
Publicado: (2022) -
Kernelized multiview signed graph learning for single-cell RNA sequencing data
por: Karaaslanli, Abdullah, et al.
Publicado: (2023) -
Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information
por: Liu, Ying, et al.
Publicado: (2012) -
Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations
por: Munia, Tamanna T. K., et al.
Publicado: (2019)