  A neural network based technique is presented, which is able to successfully
extract polynomial classification rules from labeled electroencephalogram (EEG)
signals. To represent the classification rules in an analytical form, we use
the polynomial neural networks trained by a modified Group Method of Data
Handling (GMDH). The classification rules were extracted from clinical EEG data
that were recorded from an Alzheimer patient and the sudden death risk
patients. The third data is EEG recordings that include the normal and artifact
segments. These EEG data were visually identified by medical experts. The
extracted polynomial rules verified on the testing EEG data allow to correctly
classify 72% of the risk group patients and 96.5% of the segments. These rules
performs slightly better than standard feedforward neural networks.
