Ulster University Logo

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns

Herman, P.H., Prasad, Girijesh and McGinnity, T.M. (2017) Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain-Computer Interface Classification of Motor Imagery Induced EEG Patterns. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 25 (1). pp. 29-42. [Journal article]

[img] Text - Accepted Version
2MB
[img] Text - Supplemental Material
Restricted to Repository staff only

1MB

URL: http://ieeexplore.ieee.org/document/7779090/

DOI: 10.1109/TFUZZ.2016.2637934

Abstract

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 non-stationary 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 intersession as well as within-session manifestations of non-stationary 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 neuro-feedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysisaccounting 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-lineon the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.

Item Type:Journal article
Keywords:Brain–computer interface (BCI), electroencephalogram (EEG), interval type-2 fuzzy systems, pattern recognition, real-time systems, uncertainty.
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 > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:36859
Deposited By: Professor Girijesh Prasad
Deposited On:10 Feb 2017 09:33
Last Modified:10 Feb 2017 09:33

Repository Staff Only: item control page