Information geometry meets BCI spatial filtering using divergences

Algorithms using concepts from information geometry have recently become very popular in machine learning and signal processing. These methods not only have a solid mathematical foundation but they also allow to interpret the optimization process and the solution from a geometric perspective. In thi...

Full description

Saved in:
Bibliographic Details
Published inBCI : 2014 International Winter Workshop on Brain-Computer Interface : 17-19 February 2014 pp. 1 - 4
Main Authors Samek, Wojciech, Muller, Klaus-Robert
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2014
Subjects
Brain-Computer Interfacing
Common Spatial Patterns
Covariance matrices
Divergences
Information geometry
Linear programming
Robustness
Spatial filters
Online AccessGet full text
DOI10.1109/iww-BCI.2014.6782545

Cover

More Information
Summary:Algorithms using concepts from information geometry have recently become very popular in machine learning and signal processing. These methods not only have a solid mathematical foundation but they also allow to interpret the optimization process and the solution from a geometric perspective. In this paper we apply information geometry to Brain-Computer Interfacing (BCI). More precisely, we show that the spatial filter computation in BCI can be cast into an information geometric framework based on divergence maximization. This formulation not only allows to integrate many of the recently proposed CSP algorithms in a principled manner, but also enables us to easily develop novel CSP variants with different properties. We evaluate the potentials of our information geometric framework on a data set containing recordings from 80 subjects.
DOI:10.1109/iww-BCI.2014.6782545