Fetsje Bijma

Summary

Affiliation: VU University Medical Center
Country: The Netherlands

Publications

  1. ncbi A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements
    Fetsje Bijma
    MEG Center, Department PMT, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands
    Neuroimage 20:233-43. 2003
  2. ncbi The coupled dipole model: an integrated model for multiple MEG/EEG data sets
    Fetsje Bijma
    MEG Center, Department Physics and Medical Technology, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands
    Neuroimage 23:890-904. 2004
  3. ncbi The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products
    Fetsje Bijma
    Department PMT, Vrije Universiteit Medical Center, MEG Center, De Boelelaan 1118, Amsterdam, The Netherlands
    Neuroimage 27:402-15. 2005
  4. ncbi In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head
    Sónia I Gonçalves
    MEG Centre VU University Medical Centre, P O Box 7057, 1007 MB Amsterdam, The Netherlands
    IEEE Trans Biomed Eng 50:754-67. 2003
  5. ncbi A maximum-likelihood estimator for trial-to-trial variations in noisy MEG/EEG data sets
    Jan Casper de Munck
    Department of Physics and Medical Technology of University Hospital of the Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
    IEEE Trans Biomed Eng 51:2123-8. 2004
  6. ncbi Data-driven modeling of phase interactions between spontaneous MEG oscillations
    Rikkert Hindriks
    Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands
    Hum Brain Mapp 32:1161-78. 2011

Collaborators

Detail Information

Publications6

  1. ncbi A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements
    Fetsje Bijma
    MEG Center, Department PMT, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands
    Neuroimage 20:233-43. 2003
    ..When analyzing events at a fixed sample after the stimulus (e.g., the SEF N20 response) one can take advantage of this nonstationarity by optimizing the baseline window to obtain a low noise variance at this particular sample...
  2. ncbi The coupled dipole model: an integrated model for multiple MEG/EEG data sets
    Fetsje Bijma
    MEG Center, Department Physics and Medical Technology, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands
    Neuroimage 23:890-904. 2004
    ..Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set...
  3. ncbi The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products
    Fetsje Bijma
    Department PMT, Vrije Universiteit Medical Center, MEG Center, De Boelelaan 1118, Amsterdam, The Netherlands
    Neuroimage 27:402-15. 2005
    ..The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model...
  4. ncbi In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head
    Sónia I Gonçalves
    MEG Centre VU University Medical Centre, P O Box 7057, 1007 MB Amsterdam, The Netherlands
    IEEE Trans Biomed Eng 50:754-67. 2003
    ..We show that the proposed method is suited to this goal...
  5. ncbi A maximum-likelihood estimator for trial-to-trial variations in noisy MEG/EEG data sets
    Jan Casper de Munck
    Department of Physics and Medical Technology of University Hospital of the Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
    IEEE Trans Biomed Eng 51:2123-8. 2004
    ..For these data, the advantage of applying the model is that positive and negative spikes can be processed with a single model, thereby reducing the number of degrees of freedom and increasing the signal-to-noise ratio...
  6. ncbi Data-driven modeling of phase interactions between spontaneous MEG oscillations
    Rikkert Hindriks
    Department of Mathematics, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands
    Hum Brain Mapp 32:1161-78. 2011
    ..For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics...