A Novel Method for the Characterization of Synchronization and Coupling in Multichannel EEG and ECoG
In this paper we introduce a novel method for
the characterization of synchronziation and coupling effects
in multivariate time series that can be used for the analysis
of EEG or ECoG signals recorded during epileptic seizures.
The method allows to visualize the spatio-temporal evolution
of synchronization and coupling effects that are characteristic
for epileptic seizures. Similar to other methods proposed for
this purpose our method is based on a regression analysis.
However, a more general definition of the regression together
with an effective channel selection procedure allows to use the
method even for time series that are highly correlated, which
is commonly the case in EEG/ECoG recordings with large
numbers of electrodes. The method was experimentally tested
on ECoG recordings of epileptic seizures from patients with
temporal lobe epilepsies. A comparision with the results from
a independent visual inspection by clinical experts showed
an excellent agreement with the patterns obtained with the
proposed method.
[1] R. Kus, M. Kaminski, and K. Blinowska, "Determination of EEG activity
propagation: pair-wise versus multichannel estimate," Biomedical
Engineering, IEEE Transactions on, vol. 51, pp. 1501-1510, Sept. 2004.
[2] M. J. Kaminski and K. J. Blinowska, "A new method of the description
of the information flow in the brain structures.," Biological Cybernetics,
vol. 65, 1991.
[3] M. Kaminski, M. Ding, W. Truccolo, and S. Bressler, "Evaluating causal
relations in neural systems: Granger causality, directed transfer function
and statistical assessment of significance," Biological Cybernetics,
vol. 85, 2001.
[4] L. Astolfi, F. Cincotti, D. Mattia, F. de Vico Fallani, S. Salinari,
M. Ursino, M. Zavaglia, M. Marciani, and F. Babiloni, "Estimation of the
cortical connectivity patterns during the intention of limb movements,"
Engineering in Medicine and Biology Magazine, IEEE, vol. 25, pp. 32-
38, July-Aug. 2006.
[5] A. Korzeniewska, M. Manczak, M. Kaminski, K. J. Blinowska, and
S. Kasicki, "Determination of information flow direction among brain
structures by a modified directed transfer function (dDTF) method,"
Journal of Neuroscience Methods, vol. 125, 2003.
[6] L. A. Baccala and K. Sameshima, "Partial directed coherence: a
new concept in neural structure determination," Biological Cybernetics,
vol. 84, 2001.
[7] L. Astolfi, F. Cincotti, D. Mattia, M. Marciani, L. Baccala, F. Fallani,
S. Salinari, M. Ursino, M. Zavaglia, and F. Babiloni, "Assessing cortical
functional connectivity by partial directed coherence: simulations and
application to real data," Biomedical Engineering, IEEE Transactions
on, vol. 53, pp. 1802-1812, Sept. 2006.
[8] E. Möller, B. Schack, M. Arnold, and H. Witte, "Instantaneous multivariate
eeg coherence analysis by means of adaptive high-dimensional
autoregressive models," Journal of neuroscience methods, vol. 105,
pp. 143-158, 2001.
[9] A. Neumaier and T. Schneider, "Estimation of parameters and eigenmodes
of multivariate autoregressive models," ACM Trans. Math. Softw.,
vol. 27, pp. 27-57, 2001.
[10] K. P. Burnham and D. R. Anderson, Model Selection and Multimodel
Inference: A Practical-Theoretic Approach. Springer-Verlag, 2nd ed.,
2002.
[11] H. Yanagihara, "Corrected version of aic for selecting multivariate
normal linear regression models in a general nonnormal case," J.
Multivar. Anal., vol. 97, no. 5, pp. 1070-1089, 2006.
[1] R. Kus, M. Kaminski, and K. Blinowska, "Determination of EEG activity
propagation: pair-wise versus multichannel estimate," Biomedical
Engineering, IEEE Transactions on, vol. 51, pp. 1501-1510, Sept. 2004.
[2] M. J. Kaminski and K. J. Blinowska, "A new method of the description
of the information flow in the brain structures.," Biological Cybernetics,
vol. 65, 1991.
[3] M. Kaminski, M. Ding, W. Truccolo, and S. Bressler, "Evaluating causal
relations in neural systems: Granger causality, directed transfer function
and statistical assessment of significance," Biological Cybernetics,
vol. 85, 2001.
[4] L. Astolfi, F. Cincotti, D. Mattia, F. de Vico Fallani, S. Salinari,
M. Ursino, M. Zavaglia, M. Marciani, and F. Babiloni, "Estimation of the
cortical connectivity patterns during the intention of limb movements,"
Engineering in Medicine and Biology Magazine, IEEE, vol. 25, pp. 32-
38, July-Aug. 2006.
[5] A. Korzeniewska, M. Manczak, M. Kaminski, K. J. Blinowska, and
S. Kasicki, "Determination of information flow direction among brain
structures by a modified directed transfer function (dDTF) method,"
Journal of Neuroscience Methods, vol. 125, 2003.
[6] L. A. Baccala and K. Sameshima, "Partial directed coherence: a
new concept in neural structure determination," Biological Cybernetics,
vol. 84, 2001.
[7] L. Astolfi, F. Cincotti, D. Mattia, M. Marciani, L. Baccala, F. Fallani,
S. Salinari, M. Ursino, M. Zavaglia, and F. Babiloni, "Assessing cortical
functional connectivity by partial directed coherence: simulations and
application to real data," Biomedical Engineering, IEEE Transactions
on, vol. 53, pp. 1802-1812, Sept. 2006.
[8] E. Möller, B. Schack, M. Arnold, and H. Witte, "Instantaneous multivariate
eeg coherence analysis by means of adaptive high-dimensional
autoregressive models," Journal of neuroscience methods, vol. 105,
pp. 143-158, 2001.
[9] A. Neumaier and T. Schneider, "Estimation of parameters and eigenmodes
of multivariate autoregressive models," ACM Trans. Math. Softw.,
vol. 27, pp. 27-57, 2001.
[10] K. P. Burnham and D. R. Anderson, Model Selection and Multimodel
Inference: A Practical-Theoretic Approach. Springer-Verlag, 2nd ed.,
2002.
[11] H. Yanagihara, "Corrected version of aic for selecting multivariate
normal linear regression models in a general nonnormal case," J.
Multivar. Anal., vol. 97, no. 5, pp. 1070-1089, 2006.
@article{"International Journal of Medical, Medicine and Health Sciences:57414", author = "Manfred Hartmann and Andreas Graef and Hannes Perko and Christoph Baumgartner and Tilmann Kluge", title = "A Novel Method for the Characterization of Synchronization and Coupling in Multichannel EEG and ECoG", abstract = "In this paper we introduce a novel method for
the characterization of synchronziation and coupling effects
in multivariate time series that can be used for the analysis
of EEG or ECoG signals recorded during epileptic seizures.
The method allows to visualize the spatio-temporal evolution
of synchronization and coupling effects that are characteristic
for epileptic seizures. Similar to other methods proposed for
this purpose our method is based on a regression analysis.
However, a more general definition of the regression together
with an effective channel selection procedure allows to use the
method even for time series that are highly correlated, which
is commonly the case in EEG/ECoG recordings with large
numbers of electrodes. The method was experimentally tested
on ECoG recordings of epileptic seizures from patients with
temporal lobe epilepsies. A comparision with the results from
a independent visual inspection by clinical experts showed
an excellent agreement with the patterns obtained with the
proposed method.", keywords = "EEG, epilepsy, regression analysis, seizurepropagation.", volume = "2", number = "8", pages = "287-6", }