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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

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).
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages5354-5357
Number of pages4
DOIs
Publication statusPublished - Sep 2005
Event27th International IEEE EMBS Conference, Sept., 2005, Shanghai, China -
Duration: 1 Sep 2005 → …

Conference

Conference27th International IEEE EMBS Conference, Sept., 2005, Shanghai, China
Period1/09/05 → …

Fingerprint

Brain computer interface
Electroencephalography
Fuzzy logic
Classifiers
Additive noise
Discriminant analysis
Brain
Fourier transforms

Cite this

@inproceedings{8316dc4038dd4ab5983ce1c6447ac771,
title = "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 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).",
author = "P Herman and G Prasad and TM McGinnity",
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Herman, P, Prasad, G & McGinnity, TM 2005, Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface. in Unknown Host Publication. pp. 5354-5357, 27th International IEEE EMBS Conference, Sept., 2005, Shanghai, China, 1/09/05. https://doi.org/10.1109/IEMBS.2005.1615691

Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface. / Herman, P; Prasad, G; McGinnity, TM.

Unknown Host Publication. 2005. p. 5354-5357.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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).

AB - 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).

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