Journal
NEUROIMAGE
Volume 49, Issue 2, Pages 1593-1600Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.08.027
Keywords
Information-theoretic approach; Group-based independent component analysis; Partial least square analysis; Transfer entropy
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This paper presents a data-driven pipeline for studying asymmetries in mutual interdependencies between distinct components of EEG signal. Due to volume conductance, estimating coherence between scalp electrodes may lead to spurious results. A group-based independent component analysis (ICA), which is conducted across all subjects and conditions simultaneously, is an alternative representation of the EEG measurements. Within this approach, the extracted components are independent in a global sense while short-lived or transient interdependencies may still be present between the components. In this paper, functional roles of the ICA components are specified through a partial least squares (PLS) analysis of task effects within the time Course of the derived components. Functional integration is estimated within the information-theoretic approach using transfer entropy analysis based on asymmetries in mutual interdependencies of reconstructed phase dynamics. A secondary PLS analysis is performed to assess robust task-specific changes in transfer entropy estimates between functionally specific components. (C) 2009 Elsevier Inc. All rights reserved.
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