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Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface | IEEE Conference Publication | IEEE Xplore

Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface


Abstract:

Analysis of electroencephalogram (EEG) requires a framework that facilitates handling the uncertainties associated with the varying brain dynamics and the presence of noi...Show More

Abstract:

Analysis of electroencephalogram (EEG) requires a framework that facilitates handling the uncertainties associated with the varying brain dynamics and the presence of noise. Recently, the type-2 fuzzy logic systems (T2 FLSs) have been found effective in modeling uncertain data. This paper examines the potential of the T2 FLS methodology in devising an EEG-based brain-computer interface (BCI). In particular, a T2 FLS has been designed to classify imaginary left and right hand movements based on time-frequency information extracted from the EEG with the short time Fourier transform (STFT). Robustness of the method has also been verified in the presence of additive noise. The performance of the classifier is quantified with the classification accuracy (CA). The T2 fuzzy classifier has been proven to outperform its type-1 (T1) counterpart on all data sets recorded from three subjects examined. It has also compared favorably to the well known classifier based on linear discriminant analysis (LDA)
Date of Conference: 17-18 January 2006
Date Added to IEEE Xplore: 10 April 2006
Print ISBN:0-7803-8741-4

ISSN Information:

PubMed ID: 17281461
Conference Location: Shanghai, China

References

References is not available for this document.