![]() Optimizing visual interfaces in a challenging research question.ĥ DBP: Okun/Foote Long-Term Goal: Visualize the variance in clinical outcomes (such as tremor arrest) that can be attributed to deep brain stimulation (DBS) lead location or stimulation location DBS Lead Locations for 60 Parkinson’s Disease Patients Among 8 Centers DBS Lead Locations Colored by Center Let me now turn to Visualization challenges in the Okun/Foote DBP. User interfaces depend on the level of expertise of the user. Our visual and cognitive channels can be overwhelmed with too much data. Providing users with choice in visualization is valuable, but too much choice will overwhelm. It is increasingly important to find visualizations that balance these modes of operation and identify which types of visualizations are most efficient from the perspectives of speed, accuracy, and cognitive load. Using the isovalue visualization limits the number of features visible, making concentration easier, but requiring additional interaction. However, it requires significant effort in visual search to wade through less important features. Clustering has the advantage of highlighting multiple features simultaneously. Each visualization has its own advantages and disadvantages. ![]() ΜView has a number of visualization approaches, many of which highlight the same features. The data points are colored categorically using k-means clustering with L2-norm distance metric. During the poster and demo session, you will see a poster describing mu-View. Mu-View is in the early stages of research and development, but we are excited about the possibilities of allowing our DBP partners to more effectively visualize and analyze uncertainty from their simulations and experiments. A three-dimensional view, Multiple two-dimensional views, A Feature space view using principal component analysis on the PDFs, and a Parallel coordinates view. Here we show the multi-window linked view of μView. Shown here are examples of mu-View to visualize results from a simulation study of the effects of electrical conductivity uncertainty forward and inverse electrocardiographic Simulations. Through a recent collaboration, we created mu-View, which is a software framework for uncertainty visualization and analysis. Development can be followed at Īssociated with the ECG simulation and experimental studies mentioned in the last slide, there is a need to visualize and analyze uncertainties of these simulations and experiments. ![]() Goal: Visualize the variance in clinical outcomes (such as tremor arrest) that can be attributed to deep brain stimulation (DBS) lead location or stimulation location Associated with the ECG simulation and experimental studies mentioned in the last slide, there is a need to visualize and analyze uncertainties of these simulations and experiments. While our results to date are still in the experimental phase, we have already had some success within this collaboration. nuView is being actively developed simultaneously with the development of the probabilistic atlas model and VTA simluation studies, allowing results from the simulation to be explored within nuView, and the insights gleaned from nuView to be incorporated back into the DBS model. Presentation on theme: "NView Overview We developed this tool as part of a team of visualization and biomedical researchers to better understand the physiology of DBS and patient."- Presentation transcript:ġ nView Overview We developed this tool as part of a team of visualization and biomedical researchers to better understand the physiology of DBS and patient outcome.
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