Classification of multichannel EEG patterns using parallel hidden markov models

Dror Lederman, Joseph Tabrikian

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left-right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components.

Original languageEnglish
Pages (from-to)319-328
Number of pages10
JournalMedical and Biological Engineering and Computing
Volume50
Issue number4
DOIs
StatePublished - 1 Apr 2012

Keywords

  • Brain-computer interface
  • Classification
  • Event-related potentials
  • Parallel hidden Markov model

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