Common Bayesian Network for Classification of EEG-Based Multiclass Motor Imagery BCI

Lianghua He, Die Hu, Meng Wan, Ying Wen, Karen M. Von Deneen, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

Modeling and learning of brain activity patterns represent a huge challenge to the brain-computer interface (BCI) based on electroencephalography (EEG). Many existing methods estimate the uncorrelated instantaneous demixing of EEG signals to classify multiclass motor imagery (MI). However, the condition of uncorrelation does not hold true in practice, because the brain regions work with partial or complete collaboration. This work proposes a novel method, termed as a common Bayesian network (CBN), to discriminate multiclass MI EEG signals. First, with the constraints of a Gaussian mixture model on every channel, only related channels are selected to construct a normal Bayesian network. Second, the nodes that have both common and varying edges are selected to construct a CBN. Third, the probabilities on common edges are used to learn about the support vector machine for classification. To validate the proposed method, we conduct experiments on two well-known BCI datasets and perform a numerical analysis of the propose algorithm for EEG classification in a multiclass MI BCI. Experimental results show that the proposed CBN method not only has excellent classification performance, but also is highly efficient. Hence, it is suitable for the cases where a system is required to respond within a second.

Original languageEnglish (US)
Article number7167726
Pages (from-to)843-854
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume46
Issue number6
DOIs
StatePublished - Jun 1 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Bayesian network (BN)
  • brain-computer interface (BCI)
  • electroencephalography (EEG)
  • learning algorithm
  • support vector machine (SVM)

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