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Space-time visualizations

Space-time visualizations display time along the horizontal axis and spatial position or spatial element along the vertical axis. These visualizations color-code the pixels represent each time and spatial element is to represent magnitude. Because space-time diagrams show the entire time record in one picture, you can use them to see structure if you can find a good linear ordering of the spatial elements for the vertical axis.


Shape-elements visualizations

Shape-elements visualizations can expose dynamic patterns of response and provide a spatial context for features observed in other types of visualizations.

ShapeElementsSmall.gif Shape-elements visualization
represents the spatial elements of data sets in an experiment as shapes arranged at their actual spatial positions and animates the time response by the shape colors.


Activity visualizations

Activity visualizations use thresholding to designate time periods during which elements are active or inactive. Depending on the level of thresholding, activity may correspond to spiking or to other levels of activation.


KL visualizations

KL visualizations provide charicatures of high-dimensional data sets by projecting them into two-dimensional subspaces based on optimal coordinate rotation. KL decomposition is sometimes called PCA or principal component analysis.


Wavelet visualizations

Wavelet visualizations decompose the time series associated with spatial elements into frequency bands using the discrete wavelet transformation (DWT). The different wavelet visualizations view different aspects of this frequency band behavior.

KL-wavelet visualizations

KL-wavelet visualizations perform KL decomposition on wavelet frequency bands, separating low-dimensional features into different frequency regimes.