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Hidden Markovian Modeling and Analysis of Multiple-Event-Sequence-Based Random Processes. Application to Robust Detection of Brain Functional Activation

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4 Author(s)
Faisan, S. ; LSIIT, Strasbourg I Univ., Illkirch ; Thoraval, L. ; Heitz, F. ; Armspach, J.P.

This paper presents a novel statistical approach for the modeling and analysis of structured random processes observed through multiple event sequences: the hidden Markov multiple event sequence model (HMMESM). This model accounts for several features of these processes: (i) the hidden-observable aspect of the event sequences to be analyzed, (ii) the multiplicity of the observed event sequences, (iii) the non stationary, time-localized character of their events, (iv) the redundancy, complementarity, and strong asynchrony that exist between events across sequences. A first application of this model in functional MRI (fMRI) brain mapping is presented. The developed method shows high robustness to noise and variability of the active fMRI signals

Published in:

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on  (Volume:5 )

Date of Conference:

14-19 May 2006