Speaker: Tao Ding (ShanghaiTech University)
Abstract: EEG (electroencephalogram) records brain electrical activity and is a vital clinical tool in the diagnosis and treatment of epilepsy. Time series of covariance matrices between EEG channels for patients suffering from epilepsy, obtained from an open-source dataset, are analysed. The aim of this talk is two-fold: to develop a model with interpretable parameters for different possible modes of EEG dynamics, and to explore the extent to which modelling results are affected by the choice of geometry imposed on the space of covariance matrices. The space of full-rank covariance matrices of fixed dimension forms a smooth manifold, and any statistical analysis inherently depends on the choice of metric or Riemannian structure on this manifold. The model specifies a distribution for the tangent direction vector at any time point, combining an autoregressive term, a mean reverting term and a form of Gaussian noise. Parameter inference is performed by maximum likelihood estimation, and we compare modelling results obtained using the standard Euclidean geometry and the affine invariant geometry on covariance matrices. The findings reveal distinct dynamics between epileptic seizures and interictal periods (between seizures), with interictal series characterized by strong mean reversion and absence of autoregression, while seizures exhibit significant autoregressive components with weaker mean reversion. The fitted models are also used to measure seizure dissimilarity within and between patients.
Time: 10:00am, Friday, December 6, 2024
Location: Room 1410, SIMIS, Block A, No. 657 Songhu Road, Yangpu District, Shanghai
Zoom Meeting No.: 865 9848 9352 (Passcode 678988)
About the Speaker: Dr Tao Ding is a postdoctoral researcher at ShanghaiTech University. His research interests encompass functional data analysis, machine learning, and non-Euclidean statistics, with applications across diverse data domains. He completed his PhD at Newcastle University, UK, focusing on statistical modelling and manifold-valued data in computational neuroscience.
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