Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions
Document Type
Article
Abstract
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.
Medical Subject Headings
Algorithms; Cerebral Cortex (physiology); Computer Simulation; Discriminant Analysis; Humans; Magnetoencephalography (classification, methods); Signal Processing, Computer-Assisted
Publication Date
1-1-2016
Publication Title
IEEE transactions on bio-medical engineering
E-ISSN
1558-2531
Volume
63
Issue
1
First Page
30
Last Page
42
PubMed ID
26699648
Digital Object Identifier (DOI)
10.1109/TBME.2015.2439216
Recommended Citation
Zhang, Jinyin; Li, Xin; Foldes, Stephen T.; Wang, Wei; Collinger, Jennifer L.; Weber, Douglas J.; and Bagić, Anto, "Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions" (2016). Translational Neuroscience. 2200.
https://scholar.barrowneuro.org/neurobiology/2200