基于马尔科夫切换过程的运动想象信号分类

doi:10.3969/j.issn.0258-8021.2014. 06.05

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Abstract Hidden Markov model (HMM) is well applied in brain computer interface, especially in the classification of motor imagery(MI) electroencephalogram (EEG) signal. Conventional methods use HMM to model EEG signal, then use the observed signal under controlled state to estimate the HMM parameters and finally classify the EEG signal through the trained HMMs. However, due to the characteristics of low signaltonoise ratio(SNR), high dimensionality and complexity of motor imagery EEG signal, HMM cannot fully describe the dynamic property of motor imagery EEG signal. In this paper, we use hierarchical Dirichlet process (HDP) with selfclustering ability to describe MI signals and then use AR/VAR model to highlight the time property of MI signal. Finally we combine them with Markov switching processing (MSP) so that we can get more information of MI signal. In order to verify this method, we tested the algorithm on our inhouse data and some of the 2003 BCI international competition data sets. High accuracy on classification of MI is obtained.

Key words： motor imagery rain computer interface (BCI) Markov switching processes AR model signal classification

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	WU Jun¹YANG Ya²YU ZhuLiang^1#*GU Zheng Hui¹LI YuanQing^1#

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WU Jun¹YANG Ya²YU ZhuLiang^1#*GU Zheng Hui¹LI YuanQing^1#. Motor Imagery Signal Classification Based on HDP AR HMM[J]. journal1, 2014, 33(6): 666-672.

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http://cjbme.csbme.org/EN/ 10.3969/j.issn.0258-8021.2014. 06.05 OR http://cjbme.csbme.org/EN/Y2014/V33/I6/666

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