@article {eichelbaum2014a,
	title = {Visualizing Simulated Electrical Fields from Electroencephalography and Transcranial Electric Brain Stimulation: A Comparative Evaluation},
	journal = {NeuroImage},
	year = {2014},
	note = {In Press},
	abstract = {Electrical activity of neuronal populations is a crucial aspect of brain activity. This activity is not measured directly but recorded as electrical potential changes using head surface electrodes (electroencephalogram - EEG). Head surface electrodes can also be deployed to inject electrical currents in order to modulate brain activity (transcranial electric stimulation techniques) for therapeutic and neuroscientific purposes. In electroencephalography and noninvasive electric brain stimulation, electrical fields mediate between electrical signal sources and regions of interest (ROI). These fields can be very complicated in structure, and are influenced in a complex way by the conductivity profile of the human head. Visualization techniques play a central role to grasp the nature of those fields because such techniques allow for an effective conveyance of complex data and enable quick qualitative and quantitative assessments. The examination of volume conduction effects of particular head model parameterizations (e.g., skull thickness and layering), of brain anomalies (e.g., holes in the skull, tumors), location and extent of active brain areas (e.g., high concentrations of current densities) and around current injecting electrodes can be investigated using visualization. Here, we evaluate a number of widely used visualization techniques, based on either the potential distribution or on the current-flow. In particular, we focus on the extractability of quantitative and qualitative information from the obtained images, their effective integration of anatomical context information, and their interaction. We present illustrative examples from clinically and neuroscientifically relevant cases and discuss the pros and cons of the various visualization techniques.},
	doi = {10.1016/j.neuroimage.2014.04.085},
	url = {http://www.sciencedirect.com/science/article/pii/S1053811914003656},
	author = {Eichelbaum, Sebastian and Moritz Dannhauer and Mario Hlawitschka and Dana Brooks and Thomas R. Kn{\"o}sche and Gerik Scheuermann}
}
@article {eichelbaum2012a,
	title = {LineAO {\textendash}- Improved Three-Dimensional Line Rendering},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {19},
	number = {3},
	year = {2013},
	month = {March},
	pages = {433-445},
	publisher = {IEEE Computer Society},
	address = {Los Alamitos, CA, USA},
	abstract = {Rendering large numbers of dense line bundles in three dimensions is a common need for many visualization techniques, including streamlines and fiber tractography. Unfortunately, depiction of spatial relations inside these line bundles is often difficult but critical for understanding the represented structures. Many approaches evolved for solving this problem by providing special illumination models or tube-like renderings. Although these methods improve spatial perception of individual lines or related sets of lines, they do not solve the problem for complex spatial relations between dense bundles of lines. In this paper, we present a novel approach that improves spatial and structural perception of line renderings by providing a novel ambient occlusion approach suited for line rendering in real time.},
	issn = {1077-2626},
	doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.142},
	url = {http://www.informatik.uni-leipzig.de/~ebaum/Publications/eichelbaum2012a/},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Gerik Scheuermann}
}
@conference {eichelbaum2013b,
	title = {OpenWalnut: An Open-Source Tool for Visualization of Medical and Bio-Signal Data},
	booktitle = {Biomedical Engineering / Biomedizinische Technik},
	year = {2013},
	abstract = {The number of medical imaging modalities and bio-signal acquisition methods has increased dramatically in the last years. Each is designed to answer a certain set of questions or to explore certain features of living tissue. With visualization, it is possible to combine these different types of images and data to grasp their meaning in the context of each other. Unfortunately, many existing visualization tools are focused on certain modalities and signals. With OpenWalnut, we provide a tool which is designed to be used with different signals and modalities in combination with each other. It aims at providing interactive rendering and explorability with a clean data-flow-based interface. The project is open-source and well documented and has yet been extended and used by many groups and researchers. In this short-paper, we provide a coarse overview of the principles and focus-points of OpenWalnut.},
	doi = {10.1515/bmt-2013-4183},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Gerik Scheuermann},
	editor = {Olaf D{\"o}ssel}
}
@conference {eichelbaum2013a,
	title = {PointAO - Improved Ambient Occlusion for Point-based Visualization},
	booktitle = {Proceedings of the 2013 EG Conference on Visualization},
	year = {2013},
	abstract = {The visualization of large amounts of particles, glyphs, and other point-based data plays an important role in many fields of science, among them flow mechanics, molecular dynamics, and medical imaging. The proper perception of spatial structures and spatial relatio ns in the data is crucial to the understanding. To accommodate this aspect, we utilize and improve an existing ambient occlusion approach, originally tailored towards line rendering and extend the approach to be applicable to point-based visualizations.},
	author = {Eichelbaum, Sebastian and Gerik Scheuermann and Mario Hlawitschka}
}
@inbook {eichelbaum2010a,
	title = {Fabric-Like Visualization of Tensor Field Data on Arbitrary Surfaces in Image Space},
	booktitle = {New Developments in the Visualization and Processing of Tensor Fields},
	series = {Mathematics and Visualization},
	year = {2012},
	pages = {71-92},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	abstract = {Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. It already has been shown that, by employing the metaphor of a fabric structure, tensor data can be visualized precisely on surfaces where the two eigendirections in the plane are illustrated as thread-like structures. This leads to a continuous visualization of most salient features of the tensor data set. We introduce a novel approach to compute such a visualization from tensor field data that is motivated by image-space line integral convolution (LIC). Although our approach can be applied to arbitrary, non-selfintersecting surfaces, the main focus lies on special surfaces following important features, such as surfaces aligned to the neural pathways in the human brain. By adding a postprocessing step, we are able to enhance the visual quality of the of the results, which improves perception of the major patterns.},
	isbn = {978-3-642-27342-1},
	doi = {10.1007/978-3-642-27343-8_4},
	url = {http://dx.doi.org/10.1007/978-3-642-27343-8_4},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Bernd Hamann and Gerik Scheuermann},
	editor = {Laidlaw, David H. and Vilanova, Anna}
}
@inbook {eichelbaum2010b,
	title = {Image-space Tensor Field Visualization Using a LIC-like Method},
	booktitle = {Visualization in Medicine and Life Sciences 2},
	series = {Mathematics and Visualization},
	year = {2012},
	pages = {193{\textendash}210},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	abstract = {Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. Physics-based visualization of tensor fields has proven to show the main features of symmetric second-order tensor fields, while still displaying the most important information of the data, namely the main directions in medical diffusion tensor data using texture and additional attributes using color-coding, in a continuous representation. Nevertheless, its application and usability remains limited due to its computational expensive and sensitive nature. We introduce a novel approach to compute a fabric-like texture pattern from tensor fields motivated by image-space line integral convolution (LIC). Although, our approach can be applied to arbitrary, non-selfintersecting surfaces, we are focusing on special surfaces following neural fibers in the brain. We employ a multi-pass rendering approach whose main focus lies on regaining three-dimensionality of the data under user interaction as well as being able to have a seamless transition between local and global structures including a proper visualization of degenerated points.},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Bernd Hamann and Gerik Scheuermann},
	editor = {Lars Linsen and Bernd Hamann and Hans Hagen and Hege, H.-C.}
}
@booklet {eichelbaum2012c,
	title = {Leading edge vortices of flow over a delta wing.},
	year = {2012},
	month = {November},
	author = {Eichelbaum, Sebastian and Jens Kasten and Mario Hlawitschka and Gerik Scheuermann and Bernd R. Noack}
}
@booklet {eichelbaum2012b,
	title = {Vue en tractographie d{\textquoteright}un cerveau humain.},
	year = {2012},
	month = {December},
	abstract = {Vue en tractographie d{\textquoteright}un cerveau humain. Cette technique d{\textquoteright}imagerie r{\'e}v{\`e}le la structure des fibres nerveuses - et leurs traumatismes {\'e}ventuels.},
	url = {http://www.lemonde.fr/sciences/portfolio/2012/12/27/science_1811032_1650684.html},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Gerik Scheuermann}
}
@booklet {eichelbaum2010d,
	title = {OpenWalnut: A New Tool for Multi-modal Visualization of the Human Brain},
	year = {2010},
	abstract = {In the course of the ongoing research into neurological diseases and the function and anatomy of the brain, a large variety of examination techniques has evolved. The different techniques aim at findings for different research questions or different viewpoints of a single task. Considering the different applications, it is evident that, for many research areas, only a combination of multiple techniques can help answering the posed questions. To name only one example, the combination of dwMRI and fMRI with an anatomical context provided by T1 MRI images is very common. To be able to analyze the data measured by the different techniques, a tool that can efficiently visualize the different modalities simultaneously is needed. The software (called {\em OpenWalnut}) we will present in this poster aims at exactly this task. It does not only allow to display the different modalities together, but also provides tools to analyze their interdependence and relations. In the following, we will provide you with an overview of OpenWalnut{\textquoteright}s features. Its simplicity, making it a useful visualization tool, on the one hand and its powerful and generic framework for computer scientist researchers on the other hand.},
	author = {Eichelbaum, Sebastian and Mathias Goldau and Stefan Philips and Andr{\'e} Reichenbach and Ralph Schurade and Alexander Wiebel}
}
@conference {eichelbaum2010e,
	title = {OpenWalnut - An Open-Source Visualization System},
	booktitle = {Proceedings of the 6th High-End Visualization Workshop},
	year = {2010},
	month = {December},
	pages = {67{\textendash}78},
	abstract = {In the last years a variety of open-source software packages focusing on visualization of human brain data have evolved. Many of them are designed to be used in a pure academic environment and are optimized for certain tasks or special data. The open source visualization system we introduce here is called \emph{OpenWalnut}. It is designed and developed to be used by neuroscientists during their research, which enforces the framework to be designed to be very fast and responsive on the one side, but easily extendible on the other side. \emph{OpenWalnut} is a very application-driven tool and the software is tuned to ease its use. Whereas we introduce \emph{OpenWalnut} from a user{\textquoteright}s point of view, we will focus on its architecture and strengths for visualization researchers in an academic environment.},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Alexander Wiebel and Gerik Scheuermann},
	editor = {Werner benger and Andreas Gerndt and Simon Su and Wolfram Schoor and Michael Koppitz and Wolfgang Kapferer and Hans-Peter Bischof and Massimo Di Pierro}
}
@conference {eichelbaum2010c,
	title = {Visualization of Effective Connectivity of the Brain},
	booktitle = {Proceedings of the 15th International Workshop on Vision, Modeling and Visualization (VMV) Workshop 2010},
	year = {2010},
	pages = {155-162},
	abstract = {Diffusion tensor images and higher-order diffusion images are the foundation for neuroscience researchers who are trying to gain insight into the connectome, the wiring scheme of the brain. Although modern imaging devices allow even more detailed anatomical measurements, these pure anatomical connections are not sufficient for understanding how the brain processes external stimuli. Anatomical connections constraint the causal influences between several areas of the brain, as they mediate causal influence between them. Therefore, neuroscientists developed models to represent the causal coherence between several pre-defined areas of the brain, which has been measured using fMRI, MEG, or EEG. The dynamic causal modeling (DCM) technique is one of these models and has been improved to use anatomical connection as informed priors to build the effective connectivity model. In this paper, we present a visualization method allowing neuroscientists to perceive both, the effective connectivity and the underlying anatomical connectivity in an intuitive way at the same time. The metaphor of moving information packages is used to show the relative intensity of information transfer inside the brain using a GPU based animation technique. We provide an interactive way to selectively view one or multiple effective connections while conceiving their anatomical connectivity. Additional anatomical context is supplied to give further orientation cues.},
	author = {Eichelbaum, Sebastian and Alexander Wiebel and Mario Hlawitschka and Anwander, A. and Thomas R. Kn{\"o}sche and Gerik Scheuermann},
	editor = {Reinhard Koch and Andreas Kolb and Christof Rezk-Salama}
}
@mastersthesis {eichelbaum2009,
	title = {Image Space Tensor Field Visualization Using a LIC-like Method},
	year = {2009},
	school = {Fakult{\"a}t f{\"u}r Mathematik und Informatik Universit{\"a}t Leipzig},
	type = {mastersDiploma Thesis},
	abstract = {Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. Physics-based visualization of tensor fields has proven to show the main features of symmetric second-order tensor fields, while still displaying the most important information of the data, namely the main directions in medical diffusion tensor data using texture and additional attributes using color-coding, in a continuous representation. Nevertheless, its application and usability remains limited due to its computational expensive and sensitive nature. We introduce a novel approach to compute a fabric-like texture pattern from tensor fields on arbitrary non-selfintersecting surfaces that is motivated by image space line integral convolution (LIC). Our main focus lies on regaining three-dimensionality of the data under user interaction, such as rotation and scaling. We employ a multi-pass rendering approach to estimate proper modification of the LIC noise input texture to support the three-dimensional perception during user interactions.},
	author = {Eichelbaum, Sebastian}
}
@conference {hlawitschka2008c,
	title = {Fast and Memory Efficient {GPU}-based Rendering of Tensor Data},
	booktitle = {Proceedings of the IADIS International Conference on Computer Graphics and Visualization 2008},
	year = {2008},
	pages = {36-42},
	abstract = {Graphics hardware is advancing very fast and offers new possibilities to programmers. The new features can be used in scientific visualization to move calculations from the CPU to the graphics processing unit (GPU). This is useful especially when mixing CPU intense calculations with on the fly visualization of intermediate results. We present a method to display a large amount of superquadric glyphs and demonstrate its use for visualization of measured second{\textendash}order tensor data in diffusion tensor imaging (DTI) and to stress and strain tensors of computational fluid dynamic and material simulations.},
	author = {Mario Hlawitschka and Eichelbaum, Sebastian and Gerik Scheuermann}
}
