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Hierarchical modeling of EEG signals

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3 Author(s)
Arthur C. Sanderson ; Dept. of Electrical Engng., Carnegie-Mellon Univ., Pittsburgh, PA, USA ; Jakub Segen ; E. Richey

Describes a technique for quantitative analysis of EEG signals which is based on a hierarchy of models. These models include 1) recursively estimated autoregressive model, 2) piecewise stationary autoregressive model, 3) composite source model, and 4) character string and syntactic models. This hierarchical modeling approach introduces criteria for segmentation, clustering, and identification of character substrings which reduce the need for ad hoc parameters or subjective decisions. The hierarchical representation is therefore highly operator independent, provides significant data compression of complex signals, and is compatible with a generalized classification procedure. Examples of the application of this modeling approach to clinical patient EEG data illustrate the system capabilities.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-2 ,  Issue: 5 )