Abstract
Abstract This paper presents a novel approach for the analysis of electroencephalography (EEG) signals. It is based on the distribution of the eigenvalues of a scaled Hankel matrix, which can enable us to determine the number of eigenvalues required for noise removal and signal extraction in singular spectrum analysis. This paper examines the applicability of the approach to discriminate between epileptic seizure and normal EEG signals, the extraction of attractive patterns, the filtering of EEG signals and the elimination of the noise included in the signals. Various criteria are used as features to distinguish between epileptic and normal EEG segments. The results indicate the capability of the approach for noise removal in both EEG signals, and for discrimination between them.
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