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A FUZZY LOGIC CLASSIFIER DESIGN FOR ENHANCING BCI PERFORMANCE

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

Herman, P, Prasad, G and McGinnity, TM (2006) A FUZZY LOGIC CLASSIFIER DESIGN FOR ENHANCING BCI PERFORMANCE. In: 3rd International BCI Meeting, Graz.. 3rd International BCI Meeting, Graz.. 2 pp. [Conference contribution]

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Abstract

This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.

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:18509
Deposited By:Professor Girijesh Prasad
Deposited On:16 May 2011 11:37
Last Modified:20 May 2011 15:31

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