![](bg5.png)
We plan to enlarge the data set to further increase
the quality of our models. We also want to establish a
way to combine the optometric tests of both eyes. A
comparison of brain networks to classical EEG rep-
resentations is in preparation. Furthermore we are
working on the creation of different data sets, e.g.,
communication networks and microblogs, to general-
ize our findings.
ACKNOWLEDGEMENTS
The first author thanks Carolin Gall and her students
from the Medical Faculty for collecting the EEG data.
We give thanks to Hermann Hinrichs from the Medi-
cal Faculty for pointing out several hints to preprocess
the EEG data. Last not least we thank Bernhard A.
Sabel and Michał Bola for fruitful discussions while
preparing this paper.
REFERENCES
Bailey, I. L. and Lovie, J. E. (1976). New design principles
for visual acuity letter charts. Am J Optom Physiol
Opt, 53(11):740–745.
Bunke, H. (1997). On a relation between graph edit distance
and maximum common subgraph. Pattern Recognit
Lett, 18(8):689–694.
Cs
´
ardi, G. and Nepusz, T. (2006). The igraph software
package for complex network research. InterJournal
Complex Systems, 1695.
Delorme, A. and Makeig, S. (2004). EEGLAB: an open
source toolbox for analysis of single-trial EEG dy-
namics including independent component analysis. J
Neurosci Methods, 134(1):9–21.
Edwards, E. (2007). Electrocortical Activation and Human
Brain Mapping. PhD thesis, University of California,
Berkeley, CA, USA.
Faloutsos, M., Faloutsos, P., and Faloutsos, C. (1999).
On power-law relationships of the internet topology.
In Proceedings of the conference on Applications,
technologies, architectures, and protocols for com-
puter communication, SIGCOMM ’99, pages 251–
262, New York, NY, USA. ACM.
Fischhoff, I. R., Sundaresan, S. R., Cordingley, J., Larkin,
H. M., Sellier, M., and Rubenstein, D. I. (2007).
Social relationships and reproductive state influence
leadership roles in movements of plains zebra, equus
burchellii. Anim Behav, 73(5):825–831.
Held, P., Moewes, C., Braune, C., Kruse, R., and Sabel,
B. A. (2012). Advanced analysis of dynamic graphs in
social and neural networks. In Borgelt, C., Gil, M.
´
A.,
Sousa, J. M. C., and Verleysen, M., editors, Towards
Advanced Data Analysis by Combining Soft Comput-
ing and Statistics, pages 205–222. Springer-Verlag.
Kasten, E., Wst, S., Behrens-Baumann, W., and Sabel, B. A.
(1998). Computer-based training for the treatment of
partial blindness. Nat Med, 4(9):1083–1087.
Kleinberg, J. M., Kumar, R., Raghavan, P., Rajagopalan,
S., and Tomkins, A. S. (1999). The web as a graph:
measurements, models, and methods. In Proceedings
of the 5th annual international conference on Com-
puting and combinatorics, COCOON’99, pages 1–17,
Berlin, Heidelberg. Springer-Verlag.
L
¨
utkepohl, H. (2005). New Introduction to Multiple Time
Series Analysis. Econometrics / Statistics. Springer-
Verlag, Berlin / Heidelberg, Germany.
Makeig, S., Bell, A. J., Jung, T., and Sejnowski, T. J. (1996).
Independent component analysis of electroencephalo-
graphic data. In Touretzky, D. S., Mozer, M. C.,
and Hasselmo, M. E., editors, Advances in Neural In-
formation Processing Systems, volume 8, pages 145–
151, Cambridge, MA, USA. MIT Press.
Moewes, C., Kruse, R., and Sabel, B. A. (2013). Analysis of
dynamic brain networks using var models. In Kruse,
R., Berthold, M. R., Moewes, C., Gil, M.
´
A., Grze-
gorzewski, P., and Hryniewicz, O., editors, Synergies
of Soft Computing and Statistics for Intelligent Data
Analysis, pages 525–532. Proc. of the 6th Int’l Conf.
on Soft Methods in Probability and Statistics (SMPS-
2012), 4–6 Oct. 2012, Konstanz, Germany.
Montez, T., Linkenkaer-Hansen, K., van Dijk, B. W.,
and Stam, C. J. (2006). Synchronization likelihood
with explicit time-frequency priors. Neuroimage,
33(4):1117–1125.
Pearl, J. (2009). Causal inference in statistics: An overview.
Stat Surv, 3:96–146.
Pedregosa, F., Varoquaux, G., Gramfort, and et al. (2011).
Scikit-learn: Machine learning in python. JMLR,
12:2825–2830.
Pereira-Leal, J. B., Enright, A. J., and Ouzounis, C. A.
(2004). Detection of functional modules from protein
interaction networks. Proteins: Struct Funct Bioinf,
54(1):49–57.
Sabel, B. A., Fedorov, A. B., Naue, N., Borrmann, A., Her-
rmann, C., and Gall, C. (2011). Non-invasive alternat-
ing current stimulation improves vision in optic neu-
ropathy. Restor Neurol Neurosci, 29(6):493–505.
Seabold, S. and Perktold, J. (2010). Statsmodels: Econo-
metric and statistical modeling with python. In van der
Walt, S. and Millman, J., editors, Proceedings of the
9th Python in Science Conference (SciPy 2010), pages
57–61.
Sporns, O. (2010). Networks of the Brain. MIT Press, Cam-
bridge, MA, USA.
Stam, C., Jones, B., Nolte, G., Breakspear, M., and Schel-
tens, P. (2007). Small-World networks and functional
connectivity in alzheimer’s disease. Cerebral Cortex,
17(1):92–99.
Stam, C. J. and van Dijk, B. W. (2002). Synchronization
likelihood: an unbiased measure of generalized syn-
chronization in multivariate data sets. J Phys D: Non-
linear Phenom, 163(3-4):236–251.
Varela, F., Lachaux, J., Rodriguez, E., and Martinerie, J.
(2001). The brainweb: phase synchronization and
TheEffectsofEdgeWeightsonCorrelatingDynamicalNetworks-ComparingUnweightedandWeightedBrainGraphsof
nervusopticusPatients
283