Skip to main content
padlock icon - secure page this page is secure

An fMRI-EEG Integrative Method with Model Selection Procedures for Reconstruction of Multiple Cortical Activities

Buy Article:

$68.00 + tax (Refund Policy)

Background: Neuroimaging techniques with high spatio-temporal resolution would are crucial for the advancement in brain research, improvement of clinical diagnosis and management of neuropsychiatric disorders. Functional MRI (fMRI) is characterized by its high spatial resolution. On the other hand, the techniques measuring electromagnetic features of neurons, such as electroencephalography (EEG), provide millisecond order temporal resolution. Therefore, integrative analyses of the fMRI and EEG are expected to provide information with high spatio-temporal resolution enabling to clarify dynamic multiple cortical activities

Objective: We propose a novel fMRI-EEG integrative reconstruction method for multiple cortical activities using EEG data, and we validate the accuracy of our method by comparing it with other popular reconstruction approaches that are assumed to have obtained prior information from fMRI.

Methods: We determined the first model via fMRI data, and we obtained the final model which contained the source that the fMRI could not capture through iterative model selection procedures based on the Akaike information criterion (AIC). We then used a linearly constrained generalized least-squares (LCGLS) filter to suppress unconscious activities. We carried out numerical simulations to validate the proposed method and compared it to two commonly used representative reconstructions method, sLORETA and the LCMV beamformer methods, using the residual sum of the squares.

Results: The proposed method gave a good estimation of the multiple cortical activities by suppressing other fMRI-visible and fMRI-invisible sources.

Conclusion: These results demonstrate that the proposed method can reconstruct cortical activities more accurately than either sLORETA or the LCMV beamformer methods.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: AIC; EEG inverse problem; LCGLS filter; LCMV beamformer; fMRI-EEG integrative analysis; model selection; sLORETA

Document Type: Research Article

Publication date: March 1, 2017

More about this publication?
  • NBE is the official journal of the Institute of Complex Medical Engineering (ICME). The journal promotes the best interdisciplinary research in neuroscience and biomedical engineering. NBE welcomes contributions in any domain of neural and biomedical engineering ranging in content from practical/clinical applications through experimental science, technological developments to quantitative and/or statistical methodology discussions. NBE publishes the following article types: original articles, research reviews, and book reviews.

    The goal of this journal is to establish a new forum for this interdisciplinary field so neuroscientists and biomedical engineers can publish their research works in one periodical. NBE is devoted to bridge the gap between Neuroscience and Biomedical Engineering , and to foster the mutual understanding in both fields.

    Typical topics include, but certainly are not limited, to the following:
    Neural Technology and Neural Rehabilitation
    Neurology and Signal Processing
    Biomedical Informatics
    Biomechanics and Neural Control
    Theoretical and Computational Neuroscience
    Imaging Neuroscience

    Key Features:

    - Original research papers and special issues for topics
    - Basic neurosciences, and biomedical engineering technologies
    - Existing and potential clinical applications
    - Periodic ‘Review Papers’ from world-recognized experts
    - International scope
    - No page charges
    - Rapid publication times
    - Free color in electronic version

  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more