Abstract
Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifact removal. Even when using automated procedures, most scientists engage in laborious manual examination and processing to assure high quality data and to indentify interesting or problematic data segments. Researchers also do not have a convenient method of method of visually assessing the effects of applying any stage in a processing pipeline. EEGVIS is a MATLAB toolbox that allows users to quickly explore multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. Customizable summary views reveal potentially interesting sections of data, which users can explore further by clicking to examine using detailed viewing components. The viewer and a companion browser are built on our MoBBED framework, which has a library of modular viewing components that can be mixed and matched to best reveal structure. Users can easily create new viewers for their specific data without any programming during the exploration process. These viewers automatically support pan, zoom, resizing of individual components, and cursor exploration. The toolbox can be used directly in MATLAB at any stage in a processing pipeline, as a plug-in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license at http://visual.cs.utsa.edu/eegvis.
Highlights
Recent advances in data monitoring and sensor technology have accelerated the acquisition of large, complex data sets exhibiting widely varying scales
We provide a standalone version that can be used without MATLAB or EEGLAB installed
EEGLAB offers a variety of visualizations and plug-ins for analysis and generates equivalent scripting commands that allow users to generate a scripting pipeline
Summary
Recent advances in data monitoring and sensor technology have accelerated the acquisition of large, complex data sets exhibiting widely varying scales. While normal EEG signals tend to vary on a scale of approximately 100 μV, a loose connector can result in voltages in the tens of thousands of microvolts. Both size and wide swings in scale present difficulties for plotting functions, which must display information in a limited screen area at a resolution that is comprehensible. Because of the lack of visualization tools that allow signal display on varying scales, researchers must either blindly apply processing pipelines to their data or engage in the laborious task of stepping through small blocks of data at a time. There is no convenient way to assess the effect of a particular processing step by comparing the dataset before and after the application of the algorithm
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