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Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0001-6553-823X
2017 (English)In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 25, no 1, 29-42 p.Article in journal (Refereed) Published
Abstract [en]

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data-generating mechanism. The objective of this work is, thus, to examine the applicability of the T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: 1) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery, and 2) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis, kernel Fisher discriminant, and support vector machines as well as a conventional type-1 FLS, simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.

Place, publisher, year, edition, pages
IEEE Press, 2017. Vol. 25, no 1, 29-42 p.
Keyword [en]
Brain-computer interface (BCI), electroencephalogram (EEG), interval type-2 fuzzy systems, pattern recognition, real-time systems, uncertainty
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-204097DOI: 10.1109/TFUZZ.2016.2637934ISI: 000396393100004ScopusID: 2-s2.0-85014919560OAI: oai:DiVA.org:kth-204097DiVA: diva2:1085417
Note

QC 20170329

Available from: 2017-03-29 Created: 2017-03-29 Last updated: 2017-03-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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