Task modulation of spatiotemporal dynamics in semantic brain networks: an EEG/MEG study.
TLDR
In this paper, the authors compared spatiotemporal brain dynamics between visual lexical and semantic decision tasks (LD and SD), analysing whole-cortex evoked responses and spectral functional connectivity (coherence) in source-estimated EEG and magnetoencephalography (EEG and MEG) recordings.About:
This article is published in NeuroImage.The article was published on 2021-11-29 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Magnetoencephalography & Semantic memory.read more
Citations
More filters
Journal ArticleDOI
Towards an objective evaluation of EEG/MEG source estimation methods – The linear approach
TL;DR: In this paper , a framework and tools for objective and intuitive resolution analysis of EEG/MEG source estimation based on linear systems analysis are described, and applied to the most widely used distributed source estimation methods such as L2-minimum-norm estimation (L2-MNE) and linearly constrained minimum variance (LCMV) beamformers.
Journal ArticleDOI
Multiple functions of the angular gyrus at high temporal resolution
TL;DR: In this article , the angular gyrus (AG) is evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies.
Journal ArticleDOI
Time-Lagged Multidimensional Pattern Connectivity (TL-MDPC): An EEG/MEG pattern transformation based functional connectivity metric
TL;DR: Time-lagged multidimensional pattern connectivity (TL-MDPC) as mentioned in this paper is a bivariate functional connectivity metric for EEG/MEG research, which estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges.
Posted ContentDOI
Time Lagged Multidimensional Pattern Connectivity (TL MDPC): An EEG/MEG Pattern Transformation Based Functional Connectivity Metric
TL;DR: Time-lagged multidimensional pattern connectivity (TL-MDPC) as discussed by the authors is a bivariate functional connectivity metric for EEG/MEG data that estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges.
Posted ContentDOI
Identifying nonlinear Functional Connectivity with EEG/MEG using Nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC)
TL;DR: In this paper , a nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC) metric was proposed for event-related EEG/MEG applications.
References
More filters
Journal ArticleDOI
Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
Journal ArticleDOI
Nonparametric statistical testing of EEG- and MEG-data
Eric Maris,Robert Oostenveld +1 more
TL;DR: This paper forms a null hypothesis and shows that the nonparametric test controls the false alarm rate under this null hypothesis, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect.
Journal ArticleDOI
Fast and robust fixed-point algorithms for independent component analysis
TL;DR: Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Journal ArticleDOI
A multi-modal parcellation of human cerebral cortex
Matthew F. Glasser,Timothy S. Coalson,Emma C. Robinson,Emma C. Robinson,Carl D. Hacker,John W. Harwell,Essa Yacoub,Kamil Ugurbil,Jesper L. R. Andersson,Christian F. Beckmann,Mark Jenkinson,Stephen Smith,David C. Van Essen +12 more
TL;DR: Using multi-modal magnetic resonance images from the Human Connectome Project and an objective semi-automated neuroanatomical approach, 180 areas per hemisphere are delineated bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults.
Journal ArticleDOI
Measuring phase synchrony in brain signals
TL;DR: It is argued that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction, and ways to separate such conduction effects from true signal synchrony are discussed.