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Faculty of Medicine at Catholic University of Leuven, Belgium

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15 Papers

Effect of Typicality in Abstract Word Categories on N400 ERP

Topics: Neural Signal Processing; EEG and EMG Signal Processing and Applications; Information Processing; Cognitive Science and Psychology

Authors: Mansoureh Fahimi and Marc Van Hulle

Affiliation: KU Leuven University, Belgium

Quick Abstract

We investigate the effect of word typicality-the degree of membership of a word to its superordinate category- on the N400 event-related potential (ERP) using a single trial detection approach based on spatiotemporal beamforming. Unlike the norm in studies, where mostly concrete categories are used (imaginable objects), we considered a total of 6 basic categories: three abstract and unimaginable (emotion, event, illness) one abstract yet clearly imaginable (colour), and two concrete categories, one coherent (mammals) and the other incoherent (furniture). Our results show that, independently of word abstractness or concreteness, word typicality has a clear effect on N400 both in terms of amplitude and scalp localization, which in turn is indicative of differences in difficulty of word processing.

Related Ontology Subjects/Areas/Topics (7)

EMG Signal Processing and Applications	Neural Signal Processing	NeuroSensing and Diagnosis
Health Engineering and Technology Applications	Neurocomputing	Neurotechnology, Electronics and Informatics
Information Processing

DECODING SSVEP RESPONSES BASED ON PARAFAC DECOMPOSITION

Topics: Medical Signal Acquisition, Analysis and Processing; Time and Frequency Response; Detection and Identification

Authors: Nikolay V. Manyakov , Nikolay Chumerin , Adrien Combaz , Arne Robben , Marijn van Vliet and Marc M. Van Hulle

Affiliation: K. U. Leuven, Belgium

Digital Object Identifier: 10.5220/0003853604430447

Proceedings of BIOSIGNALS 2012/BIOSTEC, ISBN: 978-989-8425-89-8

Quick Abstract

In this position paper, we investigate whether a parallel factor analysis (Parafac) decomposition is beneficial to the decoding of steady-state visual evoked potentials (SSVEP) present in electroencephalogram (EEG) recordings taken from the subject’s scalp. In particular, we develop an automatic algorithm aimed at detecting the stimulation frequency after Parafac decomposition. The results are validated on recordings made from 54 subjects using consumer-grade EEG hardware (Emotiv’s EPOC headset) in a real-world environment. The detection of one frequency among 12, 4 and 2 possible was considered to assess the feasibility for Brain Computer Interfacing (BCI). We determined the frequencies subsets, among all subjects, that maximize the detection rate.

Related Ontology Subjects/Areas/Topics (10)

Applications and Services	Detection and Identification	Signal Processing, Sensors, Systems Modeling and Control
Biomedical Engineering	Informatics in Control, Automation and Robotics	Time and Frequency Response
Biomedical Signal Processing	Medical Image Detection, Acquisition, Analysis and Processing	Time-Frequency Analysis
Computer Vision, Visualization and Computer Graphics

HIGH RESOLUTION TIME-OF-ARRIVAL FOR A CM-PRECISE SUPER 10 METER 802.15.3C-BASED 60GHZ OFDM POSITIONING APPLICATION

Topics: Location Systems and Technology; Signal Processing in Communications; Detection and Estimation

Authors: Tom Redant ¹ and Wim Dehaene ²

Affiliations: ¹ K.U. Leuven, Belgium , ² K.U. Leuven and IMEC, Belgium

Digital Object Identifier: 10.5220/0003730302710277

Proceedings of PECCS 2012, ISBN: 978-989-8565-00-6

Quick Abstract

A 802.15.3c-compatible technique for super 10 meter cm-accurate and precise ranging is introduced, achieving update rates of more than 300kHz. The implementation is realized on top of the 802.15.3c PHY High-Speed-Interface mode, specifying a multi-carrier orthogonal frequency division multiplexed (OFDM) implementation. The aimed application conditions foresee strong discrete non-line-of-sight fading conditions. The system’s performance is evaluated over these strong channel conditions. Due to the high absorption in the 60GHz band and thus the poor signal-to-noise ratio at super 10m distances the algorithm should be noise tolerant. The algorithm combines a classic auto correlation with the MLS-Prony method, a high resolution technique for frequency content analysis.

Related Ontology Subjects/Areas/Topics (6)

Detection and Estimation		Location Systems and Technology		Signal Processing in Communications
Digital Signal Processing		Mobile and Pervasive Computing		Telecommunications

INDOOR EVALUATION OF CROP ROWAND GRID DETECTION - System for an Automated Transplanter

Topic: Robotics

Authors: J. Reumers ¹ , F. De Smedt ² , J. Anthonis ¹ , H. Ramon ¹ and T. Goedemé ²

Affiliations: ¹ K.U. Leuven, Belgium , ² EAVISE and Lessius Mechelen, Belgium

Digital Object Identifier: 10.5220/0003764505590563

Proceedings of ICPRAM 2012, ISBN: 978-989-8425-99-7

Quick Abstract

If crops with considerable spacing can be arranged in a precise rectangular pattern, mechanical weeding can become an alternative to chemical methods by enabling treatment in two perpendicular directions. Realisation of such a pattern requires innovation of seedling transplanters. A computer vision-based sensing system was developed for detecting a transplanters posture relative to the crop. A method for indoor evaluation of the systems accuracy is proposed, using an experimental cart on a set of rails to control the vehicle’s posture. The method was successful in evaluating the estimates of the lateral offset and the heading angle, but the reliability of the validation values for the longitudinal distance is limited. Tests results showed that the maximum error on the measurements of the lateral offset is 1.5cm and that the standard deviation is smaller than 0.6cm. The maximum error on the angle measurements is 2:1º. The standard deviation of the error is smaller than 0:65º. The standard deviation of the error on the estimates of the longitudinal distance is typically 0.7cm.

Related Ontology Subjects/Areas/Topics (4)

Applications		Robotics		Software Engineering
Pattern Recognition

AN EVENT DISTRIBUTION PLATFORM FOR RECOMMENDING CULTURAL ACTIVITIES

Topics: Searching and Browsing; Personalized Web Sites and Services; Web Personalization; Metadata and Metamodeling; Ontology and the Semantic Web

Authors: Toon De Pessemier ¹ , Sam Coppens ¹ , Erik Mannens ² , Simon Dooms ¹ , Luc Martens ¹ and Kristof Geebelen ³

Affiliations: ¹ Ghent University, Belgium , ² Ghent University - IBBT - Multimedia Lab, Belgium , ³ K.U. Leuven, Belgium

Digital Object Identifier: 10.5220/0003193702310236

Proceedings of WEBIST 2011, ISBN: 978-989-8425-51-5

Quick Abstract

Today, people have limited leisure time which they want to fill in according to their interests. At the same time, cultural organisations offer an enormous amount of activities via their websites. This scarcity of time and the abundance of cultural events reinforce the necessity of recommender systems that assist end-users in discovering events which they are likely to enjoy. However, traditional recommender systems can not cope with event-specific restrictions such as the availability, time and location of cultural activities. Moreover, aggregating the events, collecting consistent metadata, and enriching these metadata with cross-domain knowledge pose additional challenges for the conventional distribution and recommender systems. In this paper, we show how personalised recommendation, content-based filtering, and distribution of events can be enabled by the enrichment of events metadata via open linked data sets available on the web of data. For consistency across several events providers, we propose an event model using an RDF/OWL representation of the EventsML-G2 standard. Integrating these various functionalities as an extendable bus architecture provides an open, userfriendly event distribution platform that offers the end-user a tool to access useful event information that goes beyond basic information retrieval.

Related Ontology Subjects/Areas/Topics (9)

Data Engineering	Ontology and the Semantic Web	Web Information Systems and Technologies
Metadata and Metamodeling	Personalized Web Sites and Services	Web Interfaces and Applications
Ontologies and the Semantic Web	Searching and Browsing	Web Personalization

COMPARISON OF LINEAR CLASSIFICATION METHODS FOR P300 BRAIN-COMPUTER INTERFACE ON DISABLED SUBJECTS

Topics: Pattern Recognition; Medical Signal Acquisition, Analysis and Processing; Real-Time Systems

Authors: Nikolay V. Manyakov , Nikolay Chumerin , Adrien Combaz and Marc M. Van Hulle

Affiliation: K. U. Leuven, Belgium

Digital Object Identifier: 10.5220/0003273903280334

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

Quick Abstract

In this paper, we investigate the accuracy of linear classification techniques for a P300 Brain-Computer Interface used in a typing paradigm. Fisher’s Linear Discriminant Analysis (LDA), Bayesian Linear Discriminant Analysis (BLDA), Stepwise Linear Discriminant Analysis (SLDA), linear Support Vector Machine (SVM) and a method based on Feature Extraction (FE) were compared. Experiments were performed on patients suffering from Amyotrophic Lateral Sclerosis (ALS), middle cerebral artery (MCA) stroke and Subarachnoid Hemorrhage (SAH), in on-line and off-line mode. Our results show that BLDA yields a significantly higher accuracy than the other linear techniques we have compared, at least for our group of subjects.

Related Ontology Subjects/Areas/Topics (17)

Applications and Services	Health Engineering and Technology Applications	Pattern Recognition
Artificial Intelligence	Human-Computer Interaction	Physiological Computing Systems
Biomedical Engineering	Medical Image Detection, Acquisition, Analysis and Processing	Real-Time Systems
Biomedical Signal Processing	Methodologies and Methods	Sensor Networks
Computer Vision, Visualization and Computer Graphics	Neurocomputing	Soft Computing
Data Manipulation	Neurotechnology, Electronics and Informatics

A THREE-LEVEL ARCHITECTURE FOR MODEL–FREE DETECTION AND TRACKING OF INDEPENDENTLY MOVING OBJECTS

Topics: Image-Based Modeling and 3D Reconstruction; Motion and Tracking; Stereo Vision and Structure from Motion; Detecting 3D Objects Using Patterns of Motion and Appearance

Authors: Nicolas Pugeault ¹ , Karl Pauwels ² , Mark M. Van Hulle ² , Florian Pilz ³ and Norbert Krüger ⁴

Affiliations: ¹ University of Surrey, United Kingdom , ² Katholieke Universiteit Leuven, Belgium , ³ Aalborg Universitet Copenhagen, Denmark , ⁴ University of Southern Denmark, Denmark

Digital Object Identifier: 10.5220/0002838002370244

Proceedings of VISAPP 2010/VISIGRAPP, ISBN: 978-989-674-028-3

Quick Abstract

We present a three–level architecture for detection and tracking of independently moving objects (IMOs) in sequences recorded from a moving vehicle. At the first stage, image pixels with an optical flow that is not entirely induced by the car’s motion are detected by combining dense optical flow, egomotion extracted from this optical flow, and dense stereo. These pixels are segmented and an attention mechanism is used to process them at finer resolution at the second level making use of sparse 2D and 3D edge descriptors. Based on the rich and precise information on the second level, the full rigid motion for the environment and for each IMO is computed. This motion information is then used for tracking, filtering and the building of a 3D model of the street structure as well as the IMO. This multi-level architecture allows us to combine the strength of both dense and sparse processing methods in terms of precision and computational complexity, and to dedicate more processing capacity to the important parts of the scene (the IMOs).

Related Ontology Subjects/Areas/Topics (13)

Applications	Image-Based Modeling	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Methodologies and Methods	Physiological Computing Systems
Detecting 3D Objects Using Patterns of Motion and Appearance	Motion and Tracking	Software Engineering
Geometry and Modeling	Motion, Tracking and Stereo Vision	Stereo Vision and Structure from Motion
Human-Computer Interaction

DECODING SSVEP RESPONSES USING TIME DOMAIN CLASSIFICATION

Topics: Industrial, Financial and Medical Applications; Computational Neuroscience; Neuroinformatics and Bioinformatics

Authors: Nikolay V. Manyakov , Nikolay Chumerin , Adrien Combaz , Arne Robben and Mark M. Van Hulle

Affiliation: K. U. Leuven, Belgium

Digital Object Identifier: 10.5220/0003106103760380

Proceedings of ICNC 2010/IJCCI, ISBN: 978-989-8425-32-4

Quick Abstract

In this paper, we propose a new time domain method for decoding the steady-state visual evoked potential recorded while the subject is looking at stimuli flickering with constant frequencies. Using several such stimuli, with different frequencies, a brain-computer interface can be built. We have assessed the influence of the number of electrodes on the decoding accuracy. A comparison between active wet- and bristle dry electrodes were made. The dependence between accuracy and the length of the EEG interval used for decoding was shown.

Related Ontology Subjects/Areas/Topics (22)

Artificial Intelligence	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Biomedical Engineering	Human-Computer Interaction	Pattern Recognition
Biomedical Signal Processing	Industrial, Financial and Medical Applications	Physiological Computing Systems
Computational Intelligence	Methodologies and Methods	Sensor Networks
Computational Neuroscience	Neural Networks	Signal Processing
Computer-Supported Education	Neurocomputing	Soft Computing
Domain Applications and Case Studies	Neuroinformatics and Bioinformatics	Theory and Methods
Fuzzy Systems

Maximization of the Operating Volume using HF RFID Loop Antennas

Authors: Kevin D'hoe ¹ , Nobby Stevens ¹ , Jean-Pierre Goemaere ¹ , Lieven De Strycker ¹ and Bart Nauwelaers ²

Affiliations: ¹ Catholic University College Ghent, Belgium , ² K. U. Leuven, Belgium

Digital Object Identifier: 10.5220/0003026000230029

Proceedings of IWRT 2010/ICEIS, ISBN: 978-989-8425-11-9

Quick Abstract

This article describes how a passive radio frequency identification (RFID) system is optimized. Our attention focuses on the operating volume of a circular loop antenna. Hereby the design of loop antennas is given a new approach to establish a maximum operating volume. Therefore, the off-axis magnetic field is investigated. This is done for a circular loop in free space and in the vicinity of a metal plane. A numerical approach is given towards the maximization of the operating volume. The goal is to provide new insights into the design of a loop antenna and enhance the reliability of the RFID system.

ROAD INTERPRETATION FOR DRIVER ASSISTANCE BASED ON AN EARLY COGNITIVE VISION SYSTEM

Topics: Image-Based Modeling and 3D Reconstruction; Statistical Approach; Early Vision and Image Representation; Visual Navigation; Cognitive & Biologically Inspired Vision; 2D and 3D Scene Understanding

Authors: Emre Başeski ¹ , Lars Baunegaard With Jensen ¹ , Nicolas Pugeault ¹ , Florian Pilz ² , Karl Pauwels ³ , Marc M. Van Hulle ³ , Florentin Wörgötter ⁴ and Norbert Krüger ¹

Affiliations: ¹ The Mærsk Mc-Kinney Møller Institute, Univeristy of Southern Denmark, Denmark , ² Department for Media Technology, Aalborg University Copenhagen, Denmark , ³ Laboratorium voor Neuro- en Psychofysiologie, Belgium , ⁴ Bernstein Center for Computational Neuroscience, University of Göttingen, Germany

Digital Object Identifier: 10.5220/0001796104960505

Proceedings of VISAPP 2009/VISIGRAPP, ISBN: 978-989-8111-69-2

Quick Abstract

In this work, we address the problem of road interpretation for driver assistance based on an early cognitive vision system. The structure of a road and the relevant traffic are interpreted in terms of ego-motion estimation of the car, independently moving objects on the road, lane markers and large scale maps of the road. We make use of temporal and spatial disambiguation mechanisms to increase the reliability of visually extracted 2D and 3D information. This information is then used to interpret the layout of the road by using lane markers that are detected via Bayesian reasoning. We also estimate the ego-motion of the car which is used to create large scale maps of the road and also to detect independently moving objects. Sample results for the presented algorithms are shown on a stereo image sequence, that has been collected from a structured road.

Related Ontology Subjects/Areas/Topics (11)

Applications	Image and Video Analysis	Software Engineering
Computer Vision, Visualization and Computer Graphics	Image-Based Modeling	Statistical Approach
Early Vision and Image Representation	Motion, Tracking and Stereo Vision	Visual Navigation
Geometry and Modeling	Pattern Recognition

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