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Design and on-line evaluation of type-2 fuzzy logic system-based framework for handling uncertainties in BCI classification

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

Herman, P, Prasad, G and McGinnity, TM (2008) Design and on-line evaluation of type-2 fuzzy logic system-based framework for handling uncertainties in BCI classification. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, Vancouver, BC. IEEE. 4 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/search/freesrchabstract.jsp?tp=&arnumber=4650146&queryText%3DDesign+and+On-line+Evaluation+of+T2FLS-based+Framework+for+Handling+Uncertainties+in+BCI+Classification%26openedRefinements%3D*%26searchField%3DSearch+All

DOI: 10.1109/IEMBS.2008.4650146

Abstract

The practical applicability of brain-computer interface (BCI) technology is limited due to its insufficient reliability and robustness. One of the major problems in this regard is the extensive variability and inconsistency of brain signal patterns, observed especially in electroencephalogram (EEG). This paper presents a fuzzy logic (FL) approach to the problem of handling of the resultant uncertainty effects. In particular, it outlines the design of a novel type-2 FL system (T2FLS) classifier within the framework of an EEG-based BCI, and examines its on-line applicability in the presence of short-and long-term nonstationarities of spectral EEG correlates of motor imagery (imagination of left vs. right hand movement). The developed system is shown to effectively cope with real-time constraints. In addition, a comparative post hoc analysis has revealed that the proposed T2FLS classifier outperforms conventional BCI methods, like LDA and SVM, in terms of the maximum classification accuracy (CA) rates by a relatively small, yet statistically significant, margin.

Item Type:Conference contribution (Poster)
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:12222
Deposited By:Professor Girijesh Prasad
Deposited On:09 Mar 2010 11:57
Last Modified:23 Jun 2011 11:06

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