Harmonic Hidden Markov Models for the Study of EEG Signals

Bruno Torrésani (1) , Emilie Villaron (1)

1 LATP - Laboratoire d'Analyse, Topologie, Probabilités

A new approach for modelling multichannel signals via hidden states models in the time-frequency space is described. Multichannel signals are expanded using a local cosine basis, and the (time-frequency labelled) coefficients are modelled as multivariate random variables, whose distribution is governed by a (hidden) Markov chain. Several models are described, together with maximum likelihood estimation algorithms. The model is applied to electroencephalogram data, and it is shown that variance-covariance matrices labelled by sensor and frequency indices can yield relevant informations on the analyzed signals. This is examplified by a case study on the characterization of alpha waves desynchronization in the context of multiple sclerosis disease.

Mots clés: Time-Frequency; Hidden Markov Chain; EEG signals

Domaines: Statistiques [math.ST]; Théorie [stat.TH]; Traitement du signal et de l'image [eess.SP]; Neurosciences [q-bio.NC]; Traitement du signal et de l'image [eess.SP]

Bruno Torrésani, Emilie Villaron. Harmonic Hidden Markov Models for the Study of EEG Signals. EUSIPCO 2010, Aug 2010, Aalborg, Denmark. ⟨hal-00492800⟩