Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface
2005 (English)In: PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, 2005, Vol. 5, 5354-5357 p.Conference paper (Refereed)
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).
Place, publisher, year, edition, pages
2005. Vol. 5, 5354-5357 p.
, Proceedings of annual international conference of the IEEE engineering in medicine and biology society, ISSN 1094-687X
Other Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-90092DOI: 10.1109/IEMBS.2005.1615691ISI: 000238998404233PubMedID: 17281461OAI: oai:DiVA.org:kth-90092DiVA: diva2:504124
27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005.
QC 201203012012-02-182012-02-182012-03-01Bibliographically approved