%0 Electronic Article
%A A.J. Gibberd
%+ Dept. of Stat. Sci., Univ. Coll. London, London
%A J.D.B. Nelson
%+ Dept. of Stat. Sci., Univ. Coll. London, London
%K brain dynamics
%K MR-EGM
%K epileptic seizure
%K generative multiresolution model
%K principle components
%K multiresolution exploratory graphical models
%K multivariate wavelet framework
%K cross-validation procedure
%K EEG data
%K electroencephalography data
%K tuning parameter estimation
%K predictive risk minimisation
%K sparsity analysis
%K epileptic electroencephalography data
%K principle component analysis
%K electrical dynamics
%X We consider how recently developed multi-resolution exploratory graphical models (MR-EGM) may be estimated in a practical real-world situation. A simple cross-validation procedure based on minimising predictive risk is presented as a means to estimate tuning parameters. Through the use of electroencephalography (EEG) data, we attempt to use such a procedure to build a generative (multi-resolution) model of the electrical dynamics in the brain throughout an epileptic seizure. Brain dynamics are analysed by projecting the estimated model parameters onto their principle components where we identify two clusters of seizure activity. To conclude, we discuss the interpretation of such a principle component analysis and how well we can generalise between seizures on a specific patient.
%T Sparsity in the multivariate wavelet framework: a comparative study using epileptic electroencephalography data
%B IET Conference Proceedings
%D January 2015
%P 6 .-6 .
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=5o30rhnfrcs6j.x-iet-live-01content/conferences/10.1049/cp.2015.1761
%G EN