Edinburgh Research Explorer

Discriminative Tandem Features for HMM-based EEG Classification

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

  • Chee-Ming Ting
  • Simon King
  • Sh-Hussain Salleh
  • A. K. Ariff

Related Edinburgh Organisations

Open Access permissions

Open

Documents

  • Download as Adobe PDF

    Rights statement: © Ting, C-M., King, S., Salleh, S-H., & Ariff, A. K. (2013). Discriminative Tandem Features for HMM-based EEG Classification. In Proc. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 13). IEEE.

    Accepted author manuscript, 569 KB, PDF-document

Original languageEnglish
Title of host publicationProc. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 13)
PublisherIEEE Engineering in Medicine and Biology Society
Pages3957-3960
Volume2013
ISBN (Print)1557-170X
DOIs
Publication statusPublished - 1 Jul 2013
Event 35th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) - Oskaka, Japan
Duration: 3 Jul 20137 Jul 2013

Conference

Conference 35th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)
CountryJapan
CityOskaka
Period3/07/137/07/13

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.

Download statistics

No data available

ID: 7599641