title: On AR modelling for MEG spectral estimation, data compression and classification.
abstract: The use of the autoregressive (AR) model for magnetoencephalogram (MEG) processing is examined and compared to other methods. Spectral estimation, classification and data compression of MEG signals are studied. In application to spectral estimation the AR model is compared to the classical modified periodogram method. Also, AR modelling appears to perform very successfully when used for the classification of normal and epileptic MEG signals. Finally, the 17:1 to 23:1 data compression achieved by AR modelling, along with the above-mentioned advantages, render it suitable for storage applications. For comparison, the method of feature selection via orthogonal expansion is used as a tool to achieve data reduction. It is seen that while effective, this is less drastic than the compression of data volume achieved by AR modelling.
authors.affiliation: Faculty of Electrical Engineering, Dept of Electronic and Computer Engineering, University of Thessaloniki, Greece.
authors.forename: A
authors.initials: A
authors.lastname: Angelidou
authors.forename: M G
authors.initials: MG
authors.lastname: Strintzis
authors.forename: S
authors.initials: S
authors.lastname: Panas
authors.forename: G
authors.initials: G
authors.lastname: Anogianakis
mesh_terms: D001931:Brain Mapping; D001330:Electronic Data Processing; D004827:Epilepsy / Q000175:diagnosis; D005163:Factor Analysis, Statistical; D006801:Humans; D015225:Magnetoencephalography; D008959:Models, Neurological; D015233:Models, Statistical; D012016:Reference Values; D012044:Regression Analysis