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Feature extraction from the electroencephalogram by adaptive segmentation

This paper describes the feature extraction stage of a proposed pattern recognition system aimed at automatic EEG analysis. The basic pattern-the EEG record-is split into "elementary patterns" called segments and transients, by means of a method relying on linear predictive filtering. Appropriate features, representing power spectra and the time structure of the signal, are then extracted and finally combined into a feature set representing the EEG as a whole. The quality of this representation may be assessed by comparing the original signal with its simulation from the stored features.