12,251 research outputs found
Bacteria Hunt: Evaluating multi-paradigm BCI interaction
The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated
Design of a multimodal rendering system
This paper addresses the rendering of aligned regular multimodal
datasets. It presents a general framework of multimodal data fusion
that includes several data merging methods. We also analyze the
requirements of a rendering system able to provide these different
fusion methods. On the basis of these requirements, we propose a novel
design for a multimodal rendering system. The design has been
implemented and proved showing to be efficient and flexible.Postprint (published version
Introducing Geometry in Active Learning for Image Segmentation
We propose an Active Learning approach to training a segmentation classifier
that exploits geometric priors to streamline the annotation process in 3D image
volumes. To this end, we use these priors not only to select voxels most in
need of annotation but to guarantee that they lie on 2D planar patch, which
makes it much easier to annotate than if they were randomly distributed in the
volume. A simplified version of this approach is effective in natural 2D
images. We evaluated our approach on Electron Microscopy and Magnetic Resonance
image volumes, as well as on natural images. Comparing our approach against
several accepted baselines demonstrates a marked performance increase
A Fast hierarchical traversal strategy for multimodal visualization
In the last years there is a growing demand of multimodal medical rendering systems able to visualize simultaneously data coming from different sources. This paper addresses the Direct Volume Rendering (DVR) of aligned multimodal data in medical applications. Specifically, it proposes a hierarchical representation of the multimodal data set based on the construction of a Fusion Decision Tree (FDT) that, together with a run-length encoding of the non-empty data, provides means of efficiently accessing to the data. Three different implementations of these structures are proposed. The simulations results show that the traversal of the data is fast and that the method is suitable when interactive modifications of the fusion parameters are required.Postprint (published version
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