Poster

An alignment method for group-averaging event-related neural signals at source level

Authors:
To read the file of this research, you can request a copy directly from the authors.

Abstract

Distributed imaging and spatial filtering are the most commonly-used methods for reconstructing source activity. They provide for each putative source either the patterns of activity of three orthogonal co-localized dipoles or a single resultant activity constraining the orientation. For inter-subject comparisons or visualization convenience this latter approach shall be favored. However assigned dipole polarity is likely inconsistent across subjects, along with associated waveforms polarities, which precludes group averaging. We here propose a simple method that circumvents this issue while preserving the shape of the waveforms.

No file available

Request Full-text Paper PDF

To read the file of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Book
Magnetoencephalography (MEG) is an exciting brain imaging technology that allows real-time tracking of neural activity, making it an invaluable tool for advancing our understanding of brain function. In this comprehensive introduction to MEG, Peter Hansen, Morten Kringelbach, and Riitta Salmelin have brought together the leading researchers to provide the basic tools for planning and executing MEG experiments, as well as analyzing and interpreting the resulting data. Chapters on the basics describe the fundamentals of MEG and its instrumentation, and provide guidelines for designing experiments and performing successful measurements. Chapters on data analysis present it in detail, from general concepts and assumptions to analysis of evoked responses and oscillatory background activity. Chapters on solutions propose potential solutions to the inverse problem using techniques such as minimum norm estimates, spatial filters and beamformers. Chapters on combinations elucidate how MEG can be used to complement other neuroimaging techniques. Chapters on applications provide practical examples of how to use MEG to study sensory processing and cognitive tasks, and how MEG can be used in a clinical setting. These chapters form a complete basic reference source for those interested in exploring or already using MEG that will hopefully inspire them to try to develop new, exciting approaches to designing and analyzing their own studies. This book will be a valuable resource for researchers from diverse fields, including neuroimaging, cognitive neuroscience, medical imaging, computer modelling, as well as for clinical practitioners.
  • A Achim
Achim, A. (1995). Clinical Neurophysiology, 96(6), 574-584.
  • B D Van Veen
  • W Van Drongelen
  • M Yuchtman
  • A Suzuki
Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997). IEEE Transactions on biomedical engineering, 44(9), 867-880.
Computational intelligence and neuroscience
  • R Oostenveld
  • P Fries
  • E Maris
  • J M Schoffelen
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). Computational intelligence and neuroscience, 2011, 1.