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Discriminative tandem features for HMM-based EEG classification
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013)
  • Chee-Ming Ting, PhD
  • Simon King
  • Sh-Hussain Salleh
  • A. K. Ariff
Abstract
We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2% for the LDA and MLP features respectively. We also explore portability of these features across different subjects.
Publication Date
July 3, 2013
Citation Information
Chee-Ming Ting, Simon King, Sh-Hussain Salleh and A. K. Ariff. "Discriminative tandem features for HMM-based EEG classification" 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013)
Available at: http://works.bepress.com/chee-ming_ting/10/