Heatmap Visualization of Neural Frequency Data

Abstract: Complex spatial relationships and patterns in multivariate data are relatively simple to identify visually but di cult to detect computation- ally. For this reason, Anivis, an interactive tool for visual exploration of multivariate quantitative pure serial periodic data was developed. The data has four dimensions depth, laterality, frequency and time. The data was visualized as an animated heatmap, by mapping depth and laterality to coordinates in a pixel grid and frequency to color. Transfer functions were devised to map a single variable to color through parametric curves. Anivis implemented heatmap generation through both weighted sum and deconvolution for comparison reasons. Deconvolution exhibited a to have better theoretical and practical performance. In addition to the heatmap visualization a scatter-plot was added in order to visualize the causal relationships between data points and high value areas in the heatmap visualization. Performance and applicability of the tool were tested and verified on experimental data from the Karolinksa Institute’s Department of Neuroscience.