Permutation Statistics for Connectivity Analysis between Regions of Interest in EEG and MEG Data.

ABSTRACT: Connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are unique in their ability to provide neurophysiologically meaningful spectral and temporal information non-invasively. This multi-dimensional aspect of the MEG/EEG based connectivity increases the challenges of the analysis and interpretation of the data. Many MEG/EEG studies address this complexity by using a hypothesis-driven approach, which focuses on particular regions of interest (ROI). However, if an effect is distributed unevenly over a large ROI and variable across subjects, it may not be detectable using conventional methods. Here, we propose a novel approach, which enhances the statistical power for weak and spatially discontinuous effects. This results in the ability to identify stati

SUBMITTER: Mamashli F 

PROVIDER: S-EPMC6538606 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature