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A robust method for detecting interdependences: application to intracranially recorded EEG
Open accessAbstract
We present a measure for characterizing statistical relationships between two time sequences. In contrast to commonly used measures like cross-correlations, coherence and mutual information, the proposed measure is non-symmetric and provides information about the direction of interdependence. It is closely related to recent attempts to detect generalized synchronization. However, we do not assume a strict functional relationship between the two time sequences and try to define the measure so as to be robust against noise, and to detect also weak interdependences. We apply our measure to intracranially recorded electroencephalograms of patients suffering from severe epilepsies.
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Cites
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · cited 3×
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BackgroundMethodInfluentialSauer, T., Yorke, J. A., & Casdagli, M. (1991) · Journal of Statistical Physics · doi:10.1007/bf01053745 · Cited by 2,045
Cited by
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001) · Physical Review E · doi:10.1103/physreve.64.061907 · Cited by 3,032
Self-citationDetecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019) · Science Advances · doi:10.1126/sciadv.aau4996 · Cited by 979
Can social interaction constitute social cognition?
De Jaegher, H., Di Paolo, E., & Gallagher, S. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.06.009 · Cited by 911
Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG
Sakkalis, V. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.06.020 · Cited by 637
Causal network reconstruction from time series: From theoretical assumptions to practical estimation
Runge, J. (2018) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5025050 · Cited by 422
Classification of patterns of EEG synchronization for seizure prediction
Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.09.002 · Cited by 380
Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding
Tajima, S., Yanagawa, T., Fujii, N., & Toyoizumi, T. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004537 · Cited by 76
Elisevich, K., Shukla, N., Moran, J. E., Smith, B., Schultz, L., Mason, K., Barkley, G. L., Tepley, N., Gumenyuk, V., & Bowyer, S. M. (2011) · Epilepsia · doi:10.1111/j.1528-1167.2011.02990.x · Cited by 66
Predicting Seizures from Electroencephalography Recordings: A Knowledge Transfer Strategy
Liang, J., Lu, R., Zhang, C., & Wang, F. (2016) · 2016 IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2016.27 · Cited by 49
Metadata
- Authors
- Arnhold, J., Grassberger, P., Lehnertz, K., & Elger, C. E.
- Venue
- Physica D: Nonlinear Phenomena
- Publisher
- Elsevier BV
- Published
- 1999-12
- Volume
- 134
- Issue
- 4
- Pages
- 419-430
- ISSN
- 0167-2789 (ISSN-L)
- OpenAlex
- W2157016812
- Cited by
- 439
- References
- 2
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- green
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