MURAL - Maynooth University Research Archive Library

    Independent Components of Magnetoencephalography: Single-Trial Response Onset Times

    Tang, Akaysha and Pearlmutter, Barak A. and Malaszenko, Natalie A. and Phung, Dan B. (2002) Independent Components of Magnetoencephalography: Single-Trial Response Onset Times. NeuroImage, 17 (4). pp. 1773-1789. ISSN 1053-8119

    Download (1MB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    We recently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al., 2002, Neural Comput. 14, 1827–1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly from the sensors, we found that with SOBI preprocessing, we were able to measure, among a greater proportion of trials, single-trial response onset times that are above background neuronal activity. We suggest that SOBI ICA can improve our current capability in measuring single-trial responses from human subjects using the noninvasive brain imaging method MEG.

    Item Type: Article
    Keywords: Independent Components; Magnetoencephalography; Single-Trial Response; Onset Times;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5531
    Identification Number:
    Depositing User: Barak Pearlmutter
    Date Deposited: 03 Nov 2014 15:55
    Journal or Publication Title: NeuroImage
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads