Two-layer hidden Markov models for multi-class motor imagery classification

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

13 Citations (Scopus)

Abstract

Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we design a framework of two-layer hidden Markov models (HMMs) for probabilistic classification of EEG signals. We first independently model the characteristics of EEG signals embedded in each channel for different motor imagery tasks in the lower-layer, and then represent the holistic task-related dynamic EEG patterns in the upper-layer by considering the relationships among channels. From the experimental results based on the dataset II-a of BCI Competition IV (2008), we demonstrated that our method achieved high session-to-session transfer results and was superior to previous methods.

Original languageEnglish
Title of host publicationProceedings - Workshop on Brain Decoding
Subtitle of host publicationPattern Recognition Challenges in Neuroimaging, WBD 2010 - In Conjunction with the International Conference on Pattern Recognition, ICPR 2010
Pages5-8
Number of pages4
DOIs
Publication statusPublished - 2010
EventWorkshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging, WBD 2010 - In Conjunction with the International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 2010 Aug 222010 Aug 22

Publication series

NameProceedings - Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging, WBD 2010 - In Conjunction with theInternational Conference on Pattern Recognition, ICPR 2010

Other

OtherWorkshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging, WBD 2010 - In Conjunction with the International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period10/8/2210/8/22

Keywords

  • Brain-Computer Interface (BCI)
  • Electroencephalography (EGG)
  • Hidden Markov models (HMMs)
  • Motor imagery classification

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Clinical Neurology

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