EEG Signal Classification with Super-Dirichlet Mixture Model
2012 (English)In: Processings of IEEE Statistical Signal Processing (SSP) Workshop 2012, IEEE , 2012, 440-443 p.Conference paper (Refereed)
Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related to the transient nature of the signals. To improve the classification performance based on the mDWT coefficients, we propose a new classification method by utilizing the nonnegative and sum-to-one properties of the mDWT coefficients. To this end, the distribution of the mDWT coefficients is modeled by the Dirichlet distribution and the distribution of the mDWT coefficients from more than one channels is described by a super-Dirichletmixture model (SDMM). The Fisher ratio and the generalization error estimation are applied to select relevant channels, respectively. Compared to the state-of-the-art support vector machine (SVM) based classifier, the SDMM based classifier performs more stable and shows a promising improvement, with both channel selection strategies.
Place, publisher, year, edition, pages
IEEE , 2012. 440-443 p.
EEG classification, super-Dirichlet distribution, mixture modeling, Fisher ratio, generalization error estimation, channel selection
Signal Processing Other Medical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-93630DOI: 10.1109/SSP.2012.6319726ISI: 000309943200111ScopusID: 2-s2.0-84868243972ISBN: 978-1-4673-0183-1OAI: oai:DiVA.org:kth-93630DiVA: diva2:517212
2012 IEEE Statistical Signal Processing Workshop, SSP 2012; Ann Arbor, MI; 5 August 2012 through 8 August 2012
QC 201211232012-04-222012-04-222016-04-26Bibliographically approved