title: Hidden Markov models used for the offline classification of EEG data.
abstract: Hidden Markov models (HMM) are introduced for the offline classification of single-trail EEG data in a brain-computer-interface (BCI). The HMMs are used to classify Hjorth parameters calculated from bipolar EEG data, recorded during the imagination of a left or right hand movement. The effects of different types of HMMs on the recognition rate are discussed. Furthermore a comparison of the results achieved with the linear discriminant (LD) and the HMM, is presented.
authors.affiliation: Ludwig Boltzmann Institute for Medical Informatics and Neuroinformatics, University of Technology, Graz. obermai@dpmi.tu-graz.ac.at
authors.forename: B
authors.initials: B
authors.lastname: Obermaier
authors.forename: C
authors.initials: C
authors.lastname: Guger
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
mesh_terms: D001676:Biofeedback, Psychology; D004569:Electroencephalography / Q000145:classification* / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D007092:Imagination; D008390:Markov Chains; D015233:Models, Statistical