Hidden Markov models used for the offline classification of EEG data
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Updated: Oct 19, 2026
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
B Obermaier1, C Guger, G Pfurtscheller
1Ludwig Boltzmann Institute for Medical Informatics and Neuroinformatics, University of Technology, Graz. obermai@dpmi.tu-graz.ac.at
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.
