A tool for analyzing magnetoencephalography-data based on different artificial neural networks
The localization of intracerebral dipole sources in order to detect pathological events is one purpose of magnetoencephalography (MEG). Another aspect is the analysis of brain processes and brain structures. A system consisting of two different types of Artificial Neural Networks is presented. The structure of a feed forward neural network with two layers and a learning rule designed for the task of Blind Signal Separation (BSS) is used to separate temporarily overlapping neuron activities in the brain. Based on the separated signals, the task of the second type of neural network is to determine the position and strength of the different, underlying magnetic dipoles. Several concepts of neural networks, their limits and potentials concerning both tasks of mining medical data are discussed briefly.
neural network meg bss ica medical data mining