Usefulness of autoregressive models to classify EEG segments
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As part of a study of the interand intraindividual variability of the EEG, a. number of segmentation techniques are developed to parameterize and to quantify the spontaneous EEG (4,6). These techniques are applied to split the EEG in more or less stationary intervals. After parameterization, these intervals can be classified. The hypothesis that clusters, containing similar intervals, represent the states that can be recognized within an EEG.
[1] G. Bodenstein,et al. Feature extraction from the electroencephalogram by adaptive segmentation , 1977, Proceedings of the IEEE.