Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Support vector-enhanced design of a T2FL approach to motor imagery-related EEG pattern recognition
University of Ulster. (School of Computing and Intelligent Systems)ORCID iD: 0000-0001-6553-823X
University of Ulster. (School of Computing and Intelligent Systems)
University of Ulster. (School of Computing and Intelligent Systems)
2007 (English)In: IEEE International Conference on Fuzzy Systems, 2007, 1938-1943 p.Conference paper, Published paper (Refereed)
Abstract [en]

The significance of the initialization procedure in the development of Type-2 fuzzy logic (T2FL) system-based classifiers should be highlighted considering their intrinsically non-linear nature. Initial structure identification has been recognized as a crucial stage in the design of an interval T2FL (IT2FL) classifier utilized in the framework of electroencephalogram (EEG)-based brain - computer interface (BCI). In conjunction with an efficient gradient-based learning algorithm it has allowed for robust exploitation of T2FL's capabilities to effectively handle uncertainties inherently associated with changing dynamics of electrical brain activity. This paper builds on the previous experiences in tackling the problem of inter-session classification of motor imagery (MI)-related EEG patterns. The major contribution of this work is an empirical investigation of the concept of support vector (SV) learning applied to structure identification of the IT2FL classifier. The SV-enhanced initialization scheme is found to compare favorably to both an arbitrary initialization and the clustering approach utilized in the preceding work in terms of the inter-session BCI classification performance of the fully trained IT2FLS evaluated on three subjects.

Place, publisher, year, edition, pages
2007. 1938-1943 p.
Keyword [en]
brain, classification, electroencephalography, fuzzy systems, pattern recognition
National Category
Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-90088DOI: 10.1109/FUZZY.2007.4295661ISI: 000252371500332ISBN: 1-4244-1210-2 (print)OAI: oai:DiVA.org:kth-90088DiVA: diva2:504116
Conference
2007 IEEE International Conference on Fuzzy Systems, FUZZY, London, 23 July 2007 through 26 July 2007
Note
QC 20120228Available from: 2012-02-18 Created: 2012-02-18 Last updated: 2012-02-28Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Herman, Pawel Andrzej

Search in DiVA

By author/editor
Herman, Pawel Andrzej
Information Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf