Homepage of Fetsje Bijma
Research Interests
My research is centered on statistical methods
for life sciences, in particular for neuroscience. During the past
few years I have been working on inverse problems and covariance
models for brain imaging techniques like MEG, EEG and EEG/fMRI.
Currently I'm involved in projects on optimizing the analysis of
EEG/fMRI data, on network analysis for the human brain, and on
network properties of neuron populations.
Courses
Algemene Statistiek (see www.bb.vu.nl) Zie hier
voor de informatie voor NAJAAR 2012
Algemene Statistiek voor BWI (see www.bb.vu.nl)
Experimental Design and Data Analysis (see www.bb.vu.nl)
Mathematische Methoden 1 voor MNW (see www.bb.vu.nl)
Kansrekening en Stastistiek 2 voor MNW
Statistical Data Analysis (see www.bb.vu.nl)
Project Wiskunde
Masterclass Haal Meer Uit Je Hersenen
Publications
McAssey MP*, Bijma F*,
Tarigan B, van Pelt J, van Ooijen A, de Gunst MCM: A morpho-density
approach to estimating neural connectivity, in prep, 2012 (* first
two authors contributed equally)
Ros BP, Bijma F, de Munck
JC, de Gunst MCM: Properties of models with a Kronecker product
covariance structure, in prep, 2012
Hindriks R, Jansen R, Bijma F,
Mansvelder HD, de Gunst MCM, van der Vaart AW: Unbiased estimation
of Langevin dynamics from time series with application to
hippocampal field potentials in vitro, Phys.Rev. E 84(2): 021133,
2011
Hindriks R, Bijma F, van
Dijk BW, van der Werf YD, van Someren EJW, van der Vaart AW:
Dynamics underlying spontaneous human alpha oscillations: A
data-driven approach, NeuroImage 57(2): 440-451, 2011
Hindriks R, Bijma F, van
Dijk BW, Stam CJ, van der Werf YD, van Someren EJW, de Munck JC, van
der Vaart AW: Data-Driven Modeling of Phase Interactions Between
Spontaneous MEG Oscillations, Human Brain Mapping 32(7): 1161-1178,
2011
de Munck JC, Bijma F: How
are evoked responses generated? The need for a unified mathematical
framework, Clin. Neurophys. 121(2): 127-129, 2010
de Munck JC, Bijma F:
Three-way matrix analysis, the MUSIC algorithm and the coupled
dipole model, Journ. Neurosc. Methods 183(1): 63-71, 2009
Gonçalves SI, Bijma F,
Pouwels, PWJ, Jonker MA, Kuijer JPA, Heethaar RM, Lopes da Silva FH,
De Munck JC: A Data and Model-Driven Approach to Explore
Inter-Subject Variability of Resting-State Brain Activity Using
EEG-fMRI, IEEE Journ. Sel. Top. Sign. Proc. 2(6): 944-953, 2008
Bijma F, De Munck JC: A
space-frequency analysis of MEG background processes, NeuroImage
43(3): 478-488, 2008
Bijma F: Mathematical
modelling of Magnetoencephalographic data, Ph.D. Thesis, VU
University Amsterdam, The Netherlands, 2005
Bijma F, De Munck JC,
Huizenga HM, Heethaar RM, Nehorai A: Simultaneous estimation and
testing of sources in multiple MEG data sets, IEEE Trans. Signal
Proc. Special Issue Brain Imaging, 53(9): 3449- 3460, 2005
Bijma F, De Munck JC,
Heethaar RM: The spatiotemporal MEG covariance matrix modeled as a
sum of Kronecker products, NeuroImage 27(2): 402-415, 2005
Bijma F, De Munck JC,
Böcker KBE, Huizenga HM, Heethaar RM: The Coupled Dipole Model:
an integrated model for multiple MEG/EEG data sets, NeuroImage
23(3): 890-904, 2004
De Munck JC, Bijma F, Gaura
P, Sieluzycki C, Branco MI, Heethaar RM: A maximum likelihood
estimator for trial-to-trial variations in noisy MEG/EEG data sets,
IEEE Trans. Biomed Eng. 51(12): 2123- 2128, 2004
Bijma F, De Munck JC,
Huizenga HM, Heethaar RM: A Mathematical Approach to the Temporal
Stationarity of Background Noise in MEG/EEG measurements, NeuroImage
20(1): 233-243, 2003
Gonçalves S, De Munck JC, Verbunt JPA, Bijma F, Heethaar RM, Lopes da
Silva FH: In vivo measurement of the Brain and Skull resistivities
using an EIT based method and realistic models for the head, IEEE
Trans. Biomed. Eng. 50(6): 754-767, 2003
