Title :
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
fDate :
27 Jun- 2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374892
