Dr. Marc Van Hulle
KU Leuven, Leuven, Belgium
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Academic Career Length
42Years (1985-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications18 April 2013 in Journal of Neural Engineering
Journal Article
NMNikolay Manyakov
Nikolay ManyakovNCNikolay Chumerin
Nikolay ChumerinARArne Robben
Arne Robben
ACAdrien Combaz
Adrien Combaz
MVMarijn Van Vliet
Marijn Van VlietMarc Van HulleThe performance and usability of brain-computer interfaces (BCIs) can be improved by new paradigms, stimulation methods, decoding strategies, sensor technology etc. In this study we introduce new stimulation and decoding methods for electroencephalogram (EEG)-based BCIs that have targets flickering at the same frequency but with different phases. The phase information is estimated from the EEG data, and used for target command decoding. All visual stimulation is done on a conventional (60-Hz) LCD screen. Instead of the 'on/off' visual stimulation, commonly used in phase-coded BCI, we propose one based on a sampled sinusoidal intensity profile. In order to fully exploit the circular nature of the evoked phase response, we introduce a filter feature selection procedure based on circular statistics and propose a fuzzy logic classifier designed to cope with circular information from multiple channels jointly. We show that the proposed visual stimulation enables us not only to encode more commands under the same conditions, but also to obtain EEG responses with a more stable phase. We also demonstrate that the proposed decoding approach outperforms existing ones, especially for the short time windows used. The work presented here shows how to overcome some of the limitations of screen-based visual stimulation. The superiority of the proposed decoding approach demonstrates the importance of preserving the circularity of the data during the decoding stage.
1 April 2004 in Physica D: Nonlinear Phenomena
Journal Article
30 June 2011 in IEEE Transactions on Computers
Journal Article
KPKarl Pauwels
Karl PauwelsMTMatteo Tomasi
Matteo Tomasi
JAJavier Diaz Alonso
Javier Diaz AlonsoEREduardo Ros
Eduardo Ros
Marc Van HulleLow-level computer vision algorithms have extreme computational requirements. In this work, we compare two real-time architectures developed using FPGA and GPU devices for the computation of phase-based optical flow, stereo, and local image features (energy, orientation, and phase). The presented approach requires a massive degree of parallelism to achieve real-time performance and allows us to compare FPGA and GPU design strategies and trade-offs in a much more complex scenario than previous contributions. Based on this analysis, we provide suggestions to real-time system designers for selecting the most suitable technology, and for optimizing system development on this platform, for a number of diverse applications.
1 January 2011 in Computational Intelligence and Neuroscience
Journal Article
NMNikolay Manyakov
Nikolay ManyakovNCNikolay Chumerin
Nikolay ChumerinACAdrien Combaz
Adrien Combaz
Marc Van HulleWe report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.
10 April 2003 in Physical Review E
Journal Article
The dynamical properties of electroencephalogram (EEG) segments have recently been analyzed by Andrzejak and co-workers for different recording regions and for different brain states, using the nonlinear prediction error and an estimate of the correlation dimension. In this paper, we further investigate the nonlinear properties of the EEG signals using two established nonlinear analysis methods, and introduce a “delay vector variance” (DVV) method for better characterizing a time series. The proposed DVV method is shown to enable a comprehensive characterization of the time series, allowing for a much improved classification of signal modes. This way, the analysis of Andrzejak and co-workers can be extended toward classification of different brain states. The obtained results comply with those described by Andrzejak et al., and provide complementary indications of nonlinearity in the signals.
About Me
Marc M. Van Hulle (Full Professor) received the B.Sc.Econ. and M.B.A. degrees from Hasselt University, Hasselt, Belgium in 1986 and 1991, the M.Sc. degree in electrotechnical engineering and the Ph.D. degree in applied sciences from Katholieke Universiteit Leuven (KU Leuven), Leuven, Belgium in 1990, the doctor technices degree from the Technical University of Denmark, Copenhagen, Denmark in 2003, and an honorary doctoral degree from Brest State University, Brest, Belarus in 2009. In 1992, he was with the Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Boston, MA, USA as a post-doctoral scientist. He is currently a full Professor with the KU Leuven, where he heads the Computational Neuroscience Group, Laboratorium voor Neuroen Psychofysiologie. He has authored a monograph titled Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization (John Wiley, 2000; translated into Japanese) as well as 250 technical publications. His current research interests include computational neuroscience, brain–computer interfacing, neural networks, data mining, and signal processing. Prof. Van Hulle is a member of the Belgian Royal Academy of Medicine, Spanish Royal National Academy of Pharmacy, and Institute of Spain, as well as a founding member of the Brain–Computer Interface (BCI) Society, a technical committee member of the IEEE Signal Processing Society's Machine Learning for Signal Processing, and an IEEE Fellow.