Critical comments on EEG sensor space dynamical connectivity analysis
Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal co...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
13.07.2016
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Subjects | |
Online Access | Get full text |
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Summary: | Many different analysis techniques have been developed and applied to EEG
recordings that allow one to investigate how different brain areas interact.
One particular class of methods, based on the linear parametric representation
of multiple interacting time series, is widely used to study causal
connectivity in the brain. However, the results obtained by these methods
should be interpreted with great care. The goal of this paper is to show, both
theoretically and using simulations, that results obtained by applying causal
connectivity measures on the sensor (scalp) time series do not allow
interpretation in terms of interacting brain sources. This is because 1) the
channel locations cannot be seen as an approximation of a source's anatomical
location and 2) spurious connectivity can occur between sensors. Although many
measures of causal connectivity derived from EEG sensor time series are
affected by the latter, here we will focus on the well-known time domain index
of Granger causality (GC) and on the frequency domain directed transfer
function (DTF). Using the state-space framework and designing two simulation
studies we show that mixing effects caused by volume conduction can lead to
spurious connections, detected either by time domain GC or by DTF. Therefore,
GC/DTF causal connectivity measures should be computed at the source level, or
derived within analysis frameworks that model the effects of volume conduction.
Since mixing effects can also occur in the source space, it is advised to
combine source space analysis with connectivity measures that are robust to
mixing. |
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DOI: | 10.48550/arxiv.1607.03687 |