ABSTRACT
In this chapter, the authors present three applications of unsupervised fuzzy clustering, in conjunction with various feature-extraction methods, to the electroencephalogram (EEG) signal. They provides an automated scoring of sleep stages which will match that of a human expert, while the second prepares the background for an automated forecaster of generalized epileptic seizures which will match or improve on the human expert and categorizing evoked potentials. It may have a time-variant aspect as an additional input to the epilepsy forecaster or it may be used on an existing signal pool, gathered from one or more subjects. The EEG signal, being the superficially recorded gross electrical activity of the brain, is a non-stationary, continuously fluctuating signal, characterized both by the frequency distribution of its ongoing background pattern and by the existence and form of single waves or complexes of physiological.
