A distributed computing system for multivariate time series analyses of multichannel neurophysiological data.
We present a client-server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system's performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost.
Müller
Andy
A
Department of Epileptology, Neurophysics Group, University of Bonn, Sigmund-Freud-Str. 25, D-53105 Bonn, Germany.
Osterhage
Hannes
H
Sowa
Robert
R
Andrzejak
Ralph G
RG
Mormann
Florian
F
Lehnertz
Klaus
K
D000465:Algorithms; D003195:Computer Communication Networks; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D016014:Linear Models; D015225:Magnetoencephalography; D015999:Multivariate Analysis; D009482:Neurophysiology / Q000706:statistics & numerical data*; D017711:Nonlinear Dynamics; D011381:Programming Languages; D012984:Software