TY  - CHAP
A1  - Osterhage, Hannes
A1  - Bialonski, Stephan
A1  - Staniek, Matthäus
A1  - Schindler, Kaspar
A1  - Wagner, Tobias
A1  - Elger, Christian E.
A1  - Lehnertz, Klaus
T1  - Bivariate and multivariate time series analysis techniques and their potential impact for seizure prediction
T2  - Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications
Y1  - 2008
SN  - 978-3-527-62519-2
U6  - http://dx.doi.org/10.1002/9783527625192.ch15
SP  - 189
EP  - 208
PB  - Wiley-VCH
CY  - Weinheim
ER  - 
TY  - CHAP
A1  - Lehnertz, Klaus
A1  - Bialonski, Stephan
A1  - Horstmann, Marie-Therese
A1  - Krug, Dieter
A1  - Rothkegel, Alexander
A1  - Staniek, Matthäus
A1  - Wagner, Tobias
T1  - Epilepsy
T2  - Reviews of Nonlinear Dynamics and Complexity, Volume 2
Y1  - 2010
SN  - 9783527628001
U6  - http://dx.doi.org/10.1002/9783527628001.ch5
SP  - 159
EP  - 200
PB  - Wiley-VCH
ER  - 
TY  - CHAP
A1  - Bialonski, Stephan
A1  - Lehnertz, Klaus
T1  - From time series to complex networks: an overview
T2  - Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction
N2  - The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.
Y1  - 2013
SN  - 978-981-4525-36-7
U6  - http://dx.doi.org/10.1142/9789814525350_0010
SP  - 132
EP  - 147
ER  -