Support Vector-Enhanced Design of a T2FL Approach to Motor Imagery-Related EEG Pattern Recognition

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

7 Citations (Scopus)

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1-6
Number of pages6
DOIs
Publication statusPublished - Jul 2007
EventIEEE International Conference on Fuzzy Systems, IEEE FUZZ, London, UK -
Duration: 1 Jul 2007 → …

Conference

ConferenceIEEE International Conference on Fuzzy Systems, IEEE FUZZ, London, UK
Period1/07/07 → …

Fingerprint

Electroencephalography
Fuzzy logic
Pattern recognition
Brain computer interface
Classifiers
Learning algorithms
Brain

Cite this

@inproceedings{0cbf5c307e3b409184b6f372537bcfd3,
title = "Support Vector-Enhanced Design of a T2FL Approach to Motor Imagery-Related EEG Pattern Recognition",
abstract = "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.",
author = "P Herman and G Prasad and TM McGinnity",
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Herman, P, Prasad, G & McGinnity, TM 2007, Support Vector-Enhanced Design of a T2FL Approach to Motor Imagery-Related EEG Pattern Recognition. in Unknown Host Publication. pp. 1-6, IEEE International Conference on Fuzzy Systems, IEEE FUZZ, London, UK, 1/07/07. https://doi.org/10.1109/FUZZY.2007.4295661

Support Vector-Enhanced Design of a T2FL Approach to Motor Imagery-Related EEG Pattern Recognition. / Herman, P; Prasad, G; McGinnity, TM.

Unknown Host Publication. 2007. p. 1-6.

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

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AB - 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.

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