EEG analysis using self-organisation

The electro-encephalogram (EEG) has formed the basis for the classification of sleep into several stages. The authors propose a method of sleep analysis which requires no pre-defined application of rules, and aims to give some indication of the dynamics of sleep in humans. The authors show that the use of a self-organising feature map has enabled clustering of feature vectors in a high dimensional space, from a highly complex signal, about which little prior knowledge is known. They also demonstrated that the transition trajectories between the main cluster sites are representative of three competing dynamic processes which govern the gross structure of the EEG during sleep. They are in a position to apply this method to clinical situations for which it has hitherto been impossible to analyse the sleep EEG.<
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