Neural networks for reconstruction of focal events from bioelectric/biomagnetic potentials

Author

Schlang, Martin ; Haft, Michael ; Abraham-Fuchs, Klaus

Author_Institution

Corp. Res. & Dev., Siemens AG, Munich, Germany

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3467

Abstract

When analysing bioelectric or biomagnetic fields source reconstruction plays an important role. We show how the conventional reconstruction of dipole sources can be speeded up by neural networks. In this paper several different types of neural networks and their performance are compared for this task. We show that a radial basis function network with partitioning to one yields the best recognition results especially for potentials which are superimposed with noise

Keywords

bioelectric potentials; biomagnetism; feedforward neural nets; medical computing; neurophysiology; signal reconstruction; bioelectric potentials; biomagnetic potentials; dipole sources; focal event reconstruction; neural networks; noise; partitioning; radial basis function network; Bioelectric phenomena; Biological neural networks; Biomagnetics; Brain modeling; Electric potential; Humans; Magnetic field measurement; Magnetic heads; Neural networks; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374892

Filename

374892