Title :
Hidden Markovian Modeling and Analysis of Multiple-Event-Sequence-Based Random Processes. Application to Robust Detection of Brain Functional Activation
Author :
Faisan, S. ; Thoraval, L. ; Heitz, F. ; Armspach, J.P.
Author_Institution :
LSIIT, Strasbourg I Univ., Illkirch
Abstract :
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
Keywords :
biomedical MRI; brain; hidden Markov models; image sequences; medical image processing; random processes; brain functional activation; functional MRI brain mapping; hidden Markov multiple event sequence model; hidden-observable event sequence aspect; multiple-event-sequence-based random processes; nonstationary time-localized character; robust detection; structured random processes; Active noise reduction; Brain mapping; Brain modeling; Event detection; Hidden Markov models; Magnetic resonance imaging; Noise robustness; Random processes; Signal processing; Stochastic processes;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661471
