Information geometry meets BCI: Spatial filtering using divergences

Wojciech Samek, Klaus Robert Müller

Research output: Contribution to conferencePaper

Abstract

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.

Original languageEnglish
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of
Duration: 2014 Feb 172014 Feb 19

Other

Other2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
CountryKorea, Republic of
CityGangwon
Period14/2/1714/2/19

Keywords

  • Brain-Computer Interfacing
  • Common Spatial Patterns
  • Divergences
  • Information Geometry

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Human Factors and Ergonomics

Cite this

Samek, W., & Müller, K. R. (2014). Information geometry meets BCI: Spatial filtering using divergences. Paper presented at 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of. https://doi.org/10.1109/iww-BCI.2014.6782545