Marc Van Hulle
Marc M. Van Hulle received the M.Sc. degree in electrotechnical engineering and the Ph.D. degree in applied sciences from KU Leuven, Leuven, Belgium. He also received the B.Sc.Econ. and M.B.A. degrees. He received the Doctor Technices degree from Queen Margrethe II of Denmark, in 2003, and Honorary Doctoral degrees from Brest State University, Brest, Belarus, in 2009, and from the Yerevan State Medical University, Yerevan, Armenia, in 2013. He is currently a Full Professor at the K.U. Leuven Medical School, where he heads the Computational Neuroscience Group of the Laboratorium voor Neuro- en Psychofysiologie. Prof. Van Hulle is an IEEE Fellow.
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Papers by Marc Van Hulle
cancelling and separating slow-varying signals is introduced. The
network's weights are continuously modified using a fast unsupervised
competitive learning rule, called Fast Boundary Adaptation Rule or
FBAR, performing adaptive scalar quantization of the input signal. The
rule maximizes information-theoretic entropy and yields a
non-parametric model of the input probability density function.
Contrary to classic unsupervised competitive learning, our system
adapts its own learning rate, and hence does not require a "cooling
scheme." Furthermore, contrary to most of the other noise cancelling
approaches, our system does not require a priori knowledge or an
explicit model of the joint noise and signal characteristics.
of fMRI data, the problem of temporal autocorrelations in the residual
signal (after regression) has been frequently addressed in the open
literature. There exist various methods for correcting the ensuing
bias in the statistical testing, among which the prewhitening strategy,
which uses a prewhitening matrix for rendering the residual signal white
({\em i.e.\/}, without temporal autocorrelations). Since this correction
is only exact when the autocorrelation structure of the noise-generating
process is accurately known, the estimates derived from the fMRI data are
too noisy to be used for correction. Recently, Worsley and co-workers
proposed to spatially smooth the noisy autocorrelation estimates,
effectively reducing their variance and allowing for a better correction.
In this article, a systematic study into the effect of the smoothing
kernel width is performed and a method is introduced for choosing this
bandwidth in an `optimal' manner. Several aspects of the prewhitening
strategy are investigated, namely the choice of the autocorrelation
estimate (biased or unbiased), the accuracy of the estimates, the degree
of spatial regularisation and the order of the autoregressive model
used for characterising the noise. The proposed method is extensively
evaluated on both synthetic and real fMRI data.
cancelling and separating slow-varying signals is introduced. The
network's weights are continuously modified using a fast unsupervised
competitive learning rule, called Fast Boundary Adaptation Rule or
FBAR, performing adaptive scalar quantization of the input signal. The
rule maximizes information-theoretic entropy and yields a
non-parametric model of the input probability density function.
Contrary to classic unsupervised competitive learning, our system
adapts its own learning rate, and hence does not require a "cooling
scheme." Furthermore, contrary to most of the other noise cancelling
approaches, our system does not require a priori knowledge or an
explicit model of the joint noise and signal characteristics.
of fMRI data, the problem of temporal autocorrelations in the residual
signal (after regression) has been frequently addressed in the open
literature. There exist various methods for correcting the ensuing
bias in the statistical testing, among which the prewhitening strategy,
which uses a prewhitening matrix for rendering the residual signal white
({\em i.e.\/}, without temporal autocorrelations). Since this correction
is only exact when the autocorrelation structure of the noise-generating
process is accurately known, the estimates derived from the fMRI data are
too noisy to be used for correction. Recently, Worsley and co-workers
proposed to spatially smooth the noisy autocorrelation estimates,
effectively reducing their variance and allowing for a better correction.
In this article, a systematic study into the effect of the smoothing
kernel width is performed and a method is introduced for choosing this
bandwidth in an `optimal' manner. Several aspects of the prewhitening
strategy are investigated, namely the choice of the autocorrelation
estimate (biased or unbiased), the accuracy of the estimates, the degree
of spatial regularisation and the order of the autoregressive model
used for characterising the noise. The proposed method is extensively
evaluated on both synthetic and real fMRI data.