Publication detail

Generalized EEG-fMRI Spectral and Spatiospectral Heuristic Models

LABOUNEK, R. JANEČEK, D. MAREČEK, R. LAMOŠ, M. SLAVÍČEK, T. MIKL, M. BAŠTINEC, J. BEDNAŘÍK, P. BRIDWELL, D. BRÁZDIL, M. JAN, J.

Original Title

Generalized EEG-fMRI Spectral and Spatiospectral Heuristic Models

English Title

Generalized EEG-fMRI Spectral and Spatiospectral Heuristic Models

Type

conference paper

Language

en

Original Abstract

The aim of the current study is visualization of task-related variability in EEG-fMRI data, performed as a blind-search analysis without stimulus timings, using a methodology that is based on Kilner’s et al. heuristic approach [2]. We show that filters of the relative EEG spectra with different frequency responses visualize different task-related brain networks. The effect is more pronounced within an event-related oddball paradigm (i.e. detecting rare visual targets) than within a block-design semantic decision paradigm (i.e. detecting semantic errors). The mutual information between different EEG-fMRI activation maps calculated with filters of different frequency responses appears stable between the different paradigms. We also introduce preliminary results implementing the heuristic analysis with spatiospectral EEG components, where the filter response has two dimensions and depends on frequency and channels.

English abstract

The aim of the current study is visualization of task-related variability in EEG-fMRI data, performed as a blind-search analysis without stimulus timings, using a methodology that is based on Kilner’s et al. heuristic approach [2]. We show that filters of the relative EEG spectra with different frequency responses visualize different task-related brain networks. The effect is more pronounced within an event-related oddball paradigm (i.e. detecting rare visual targets) than within a block-design semantic decision paradigm (i.e. detecting semantic errors). The mutual information between different EEG-fMRI activation maps calculated with filters of different frequency responses appears stable between the different paradigms. We also introduce preliminary results implementing the heuristic analysis with spatiospectral EEG components, where the filter response has two dimensions and depends on frequency and channels.

Keywords

Simultaneous EEG-fMRI, heuristic model, GLM, ICA

Released

13.04.2016

Publisher

IEEE

Location

Prague

ISBN

978-1-4799-2350-2

Book

Proceedings of the International Symposium on Biomedical Imaging: From Nano to Macro

Pages from

767

Pages to

770

Pages count

4

URL

BibTex


@inproceedings{BUT123876,
  author="René {Labounek} and David {Janeček} and Radek {Mareček} and Martin {Lamoš} and Tomáš {Slavíček} and Michal {Mikl} and Jaromír {Baštinec} and Petr {Bednařík} and David {Bridwell} and Milan {Brázdil} and Jiří {Jan}",
  title="Generalized EEG-fMRI Spectral and Spatiospectral Heuristic Models",
  annote="The aim of the current study is visualization of task-related variability in EEG-fMRI data, performed as a blind-search analysis without stimulus timings, using a methodology that is based on Kilner’s et al. heuristic approach [2]. We show that filters of the relative EEG spectra with different frequency responses visualize different task-related brain networks. The effect is more pronounced within an event-related oddball paradigm (i.e. detecting rare visual targets) than within a block-design semantic decision paradigm (i.e. detecting semantic errors). The mutual information between different EEG-fMRI activation maps calculated with filters of different frequency responses appears stable between the different paradigms. We also introduce preliminary results implementing the heuristic analysis with spatiospectral EEG components, where the filter response has two dimensions and depends on frequency and channels.",
  address="IEEE",
  booktitle="Proceedings of the International Symposium on Biomedical Imaging: From Nano to Macro",
  chapter="123876",
  doi="10.1109/ISBI.2016.7493379",
  howpublished="online",
  institution="IEEE",
  year="2016",
  month="april",
  pages="767--770",
  publisher="IEEE",
  type="conference paper"
}