English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Multi-class Common Spatial Pattern and Information Theoretic Feature Extraction

Grosse-Wentrup, M., & Buss, M. (2008). Multi-class Common Spatial Pattern and Information Theoretic Feature Extraction. IEEE Transactions on Biomedical Engineering, 55(8), 1991-2000. doi:10.1109/TBME.2008.921154.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C78D-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-2FD1-D
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Grosse-Wentrup, M1, Author              
Buss, M, Author
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain--computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4 in comparison to multiclass CSP.

Details

show
hide
Language(s):
 Dates: 2008-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/TBME.2008.921154
BibTex Citekey: 4986
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE Transactions on Biomedical Engineering
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 55 (8) Sequence Number: - Start / End Page: 1991 - 2000 Identifier: ISSN: 0018-9294
CoNE: https://pure.mpg.de/cone/journals/resource/991042742034490