Decoding brain states based on magnetoencephalography from prespecified cortical regions

Jinyin Zhang, Xin Li, Stephen T. Foldes, Wei Wang, Jennifer L. Collinger, Douglas J. Weber, Anto Bagić

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Brain state decoding based on whole-head MEG has been extensively studied over the past decade. Recent MEG applications pose an emerging need of decoding brain states based on MEG signals originating from prespecified cortical regions. Toward this goal, we propose a novel region-of-interest-constrained discriminant analysis algorithm (RDA) in this paper. RDA integrates linear classification and beamspace transformation into a unified framework by formulating a constrained optimization problem. Our experimental results based on human subjects demonstrate that RDA can efficiently extract the discriminant pattern from prespecified cortical regions to accurately distinguish different brain states.

Original languageEnglish
Article number2439216
Pages (from-to)30-42
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Issue number1
StatePublished - Jan 2016


  • Decoding
  • Discriminant analysis
  • Magnetoencephalography (MEG)
  • Regions of interest (ROI)


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