June, Thursday 29th14:30 (Shannon amphitheatre, building 660) (see location):
Title: Information Geometry: A framework for manipulation and classification of neural time series
Abstract:Information geometry considers probability distributions as points of a Riemannian manifold, and provides a natural metric through which it is possible to derive a set of geometric tools (distance, tangent, mean or median ...) allowing us to analyze, interpret and classify these points while keeping intact their information content. In this presentation, we will see how to apply this framework in the context of neural timeseries (EEG and MEG) and its application to Brain Computer Interface and Neuroscience research. To this end, epochs of M/EEG signals are represented by their multivariate normal distributions and treated as points of such manifold, allowing us to benefit from the multiple properties of the corresponding Riemannian metric. The theoretical link between this approach and sources separation methods will be shown, and examples of practical use of this framework will be presented.
Contact: guillaume.charpiat at inria.fr