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The Analytic Bilinear Discrimination of Single-Trial EEG Signals in Rapid Image Triage

Author

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  • Ke Yu
  • Hasan AI-Nashash
  • Nitish Thakor
  • Xiaoping Li

Abstract

The linear discriminant analysis (LDA) method is a classical and commonly utilized technique for dimensionality reduction and classification in brain-computer interface (BCI) systems. Being a first-order discriminator, LDA is usually preceded by the feature extraction of electroencephalogram (EEG) signals, as multi-density EEG data are of second order. In this study, an analytic bilinear classification method which inherits and extends LDA is proposed. This method considers 2-dimentional EEG signals as the feature input and performs classification using the optimized complex-valued bilinear projections. Without being transformed into frequency domain, the complex-valued bilinear projections essentially spatially and temporally modulate the phases and magnitudes of slow event-related potentials (ERPs) elicited by distinct brain states in the sense that they become more separable. The results show that the proposed method has demonstrated its discriminating capability in the development of a rapid image triage (RIT) system, which is a challenging variant of BCIs due to the fast presentation speed and consequently overlapping of ERPs.

Suggested Citation

  • Ke Yu & Hasan AI-Nashash & Nitish Thakor & Xiaoping Li, 2014. "The Analytic Bilinear Discrimination of Single-Trial EEG Signals in Rapid Image Triage," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-10, June.
    • Handle: RePEc:plo:pone00:0100097
    DOI: 10.1371/journal.pone.0100097
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    References listed on IDEAS

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    1. Rui Zhang & Peng Xu & Lanjin Guo & Yangsong Zhang & Peiyang Li & Dezhong Yao, 2013. "Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
    2. Ke Yu & Yue Wang & Kaiquan Shen & Xiaoping Li, 2013. "The Synergy between Complex Channel-Specific FIR Filter and Spatial Filter for Single-Trial EEG Classification," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-7, October.
    3. Peng Xu & Ping Yang & Xu Lei & Dezhong Yao, 2011. "An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-11, January.
    4. Leigh R. Hochberg & Mijail D. Serruya & Gerhard M. Friehs & Jon A. Mukand & Maryam Saleh & Abraham H. Caplan & Almut Branner & David Chen & Richard D. Penn & John P. Donoghue, 2006. "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, Nature, vol. 442(7099), pages 164-171, July.
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    1. Jukka-Pekka Kauppi & Janne Hahne & Klaus-Robert Müller & Aapo Hyvärinen, 2015. "Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-17, June.

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