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

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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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages4242-4245
Number of pages4
DOIs
Publication statusPublished - Oct 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008 - Vancouver, BC
Duration: 1 Oct 2008 → …

Conference

Conference30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
Period1/10/08 → …

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