  In this chapter we describe new neural-network techniques developed for
visual mining clinical electroencephalograms (EEGs), the weak electrical
potentials invoked by brain activity. These techniques exploit fruitful ideas
of Group Method of Data Handling (GMDH). Section 2 briefly describes the
standard neural-network techniques which are able to learn well-suited
classification modes from data presented by relevant features. Section 3
introduces an evolving cascade neural network technique which adds new input
nodes as well as new neurons to the network while the training error decreases.
This algorithm is applied to recognize artifacts in the clinical EEGs. Section
4 presents the GMDH-type polynomial networks learnt from data. We applied this
technique to distinguish the EEGs recorded from an Alzheimer and a healthy
patient as well as recognize EEG artifacts. Section 5 describes the new
neural-network technique developed to induce multi-class concepts from data. We
used this technique for inducing a 16-class concept from the large-scale
clinical EEG data. Finally we discuss perspectives of applying the
neural-network techniques to clinical EEGs.
