Information geometry meets BCI

Spatial filtering using divergences

Wojciech Samek, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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
Title of host publication2014 International Winter Workshop on Brain-Computer Interface, BCI 2014
PublisherIEEE Computer Society
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

Fingerprint

divergence
Brain
brain
mathematics
Geometry
Learning systems
Signal processing
recording
learning

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., & Muller, K. (2014). Information geometry meets BCI: Spatial filtering using divergences. In 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 [6782545] IEEE Computer Society. https://doi.org/10.1109/iww-BCI.2014.6782545

Information geometry meets BCI : Spatial filtering using divergences. / Samek, Wojciech; Muller, Klaus.

2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society, 2014. 6782545.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Samek, W & Muller, K 2014, Information geometry meets BCI: Spatial filtering using divergences. in 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014., 6782545, IEEE Computer Society, 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014, Gangwon, Korea, Republic of, 14/2/17. https://doi.org/10.1109/iww-BCI.2014.6782545
Samek W, Muller K. Information geometry meets BCI: Spatial filtering using divergences. In 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society. 2014. 6782545 https://doi.org/10.1109/iww-BCI.2014.6782545
Samek, Wojciech ; Muller, Klaus. / Information geometry meets BCI : Spatial filtering using divergences. 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014. IEEE Computer Society, 2014.
@inproceedings{60d43177c1f74da0bb1edb81dfb8478a,
title = "Information geometry meets BCI: Spatial filtering using divergences",
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.",
keywords = "Brain-Computer Interfacing, Common Spatial Patterns, Divergences, Information Geometry",
author = "Wojciech Samek and Klaus Muller",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/iww-BCI.2014.6782545",
language = "English",
booktitle = "2014 International Winter Workshop on Brain-Computer Interface, BCI 2014",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Information geometry meets BCI

T2 - Spatial filtering using divergences

AU - Samek, Wojciech

AU - Muller, Klaus

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - Brain-Computer Interfacing

KW - Common Spatial Patterns

KW - Divergences

KW - Information Geometry

UR - http://www.scopus.com/inward/record.url?scp=84899422663&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899422663&partnerID=8YFLogxK

U2 - 10.1109/iww-BCI.2014.6782545

DO - 10.1109/iww-BCI.2014.6782545

M3 - Conference contribution

BT - 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014

PB - IEEE Computer Society

ER -