Connectivity measures applied to human brain electrophysiological data
| Autor: | Alexei Ossadtchi, Mark E. Pflieger, R.E. Greenblatt |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer science
Models Neurological Electrophysiological Phenomena Bivariate analysis Electroencephalography Machine learning computer.software_genre Article Neural Pathways medicine Humans Coherence (signal processing) Set (psychology) Models Statistical Quantitative Biology::Neurons and Cognition medicine.diagnostic_test business.industry General Neuroscience Brain Signal Processing Computer-Assisted Mutual information Magnetoencephalography Phase synchronization Artificial intelligence Nerve Net business computer |
| Zdroj: | Journal of Neuroscience Methods. 207:1-16 |
| ISSN: | 0165-0270 |
| DOI: | 10.1016/j.jneumeth.2012.02.025 |
| Popis: | Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space-time, space-frequency, and space-time-frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of cross-time-frequency measures is introduced, including a cross-time-frequency phase synchronization measure. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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