Ulster University Logo

Ulster Institutional Repository

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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Herman, P, Prasad, G and McGinnity, TM (2007) Support Vector-Enhanced Design of a T2FL Approach to Motor Imagery-Related EEG Pattern Recognition. In: IEEE International Conference on Fuzzy Systems, IEEE FUZZ, London, UK. IEEE. 6 pp. [Conference contribution]

Full text not available from this repository.

URL: http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=4295661&queryText%3DSupport+Vector-Enhanced+Design+of+a+Type-2+Fuzzy+Logic+Approach+to+Motor+Imagery-Related+EEG+Pattern++Recognition%26openedRefinements%3D*%26searchField%3DSearch+All

DOI: 10.1109/FUZZY.2007.4295661

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.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:18512
Deposited By:Professor Girijesh Prasad
Deposited On:16 May 2011 11:34
Last Modified:16 May 2011 11:34

Repository Staff Only: item control page