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Critical comments on EEG sensor space dynamical connectivity analysis

(2019) BRAIN TOPOGRAPHY. 32(4). p.643-654
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The integrative neuroscience of behavioral control (Neuroscience)
Abstract
any 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.
Keywords
brain connectivity, granger causality, source modelling

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Citation

Please use this url to cite or link to this publication:

MLA
Van de Steen, Frederik et al. “Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis.” BRAIN TOPOGRAPHY 32.4 (2019): 643–654. Print.
APA
Van de Steen, Frederik, Faes, L., Karahan, E., Songsiri, J., Valdés Sosa, P. A., & Marinazzo, D. (2019). Critical comments on EEG sensor space dynamical connectivity analysis. BRAIN TOPOGRAPHY, 32(4), 643–654.
Chicago author-date
Van de Steen, Frederik, Luca Faes, Esin Karahan, Jitkomut Songsiri, Pedro Antonio Valdés Sosa, and Daniele Marinazzo. 2019. “Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis.” Brain Topography 32 (4): 643–654.
Chicago author-date (all authors)
Van de Steen, Frederik, Luca Faes, Esin Karahan, Jitkomut Songsiri, Pedro Antonio Valdés Sosa, and Daniele Marinazzo. 2019. “Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis.” Brain Topography 32 (4): 643–654.
Vancouver
1.
Van de Steen F, Faes L, Karahan E, Songsiri J, Valdés Sosa PA, Marinazzo D. Critical comments on EEG sensor space dynamical connectivity analysis. BRAIN TOPOGRAPHY. 2019;32(4):643–54.
IEEE
[1]
F. Van de Steen, L. Faes, E. Karahan, J. Songsiri, P. A. Valdés Sosa, and D. Marinazzo, “Critical comments on EEG sensor space dynamical connectivity analysis,” BRAIN TOPOGRAPHY, vol. 32, no. 4, pp. 643–654, 2019.
@article{8203667,
  abstract     = {any 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.},
  author       = {Van de Steen, Frederik and Faes, Luca and Karahan, Esin and Songsiri, Jitkomut and Valdés Sosa, Pedro Antonio and Marinazzo, Daniele},
  issn         = {0896-0267},
  journal      = {BRAIN TOPOGRAPHY},
  keywords     = {brain connectivity,granger causality,source modelling},
  language     = {eng},
  number       = {4},
  pages        = {643--654},
  title        = {Critical comments on EEG sensor space dynamical connectivity analysis},
  url          = {http://dx.doi.org/10.1007/s10548-016-0538-7},
  volume       = {32},
  year         = {2019},
}

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