TBSSvis: Visual Analytics for Temporal Blind Source Separation

Abstract

Temporal Blind Source Separation (TBSS) is used to obtain the true, underlying processes from noisy temporal multivariate data, such as electrocardiograms. While these algorithms are widely used, the involved tasks are not well supported in current visualization tools, which offer only textbased interactions and static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and ensembles of time series. Additionally, parameters have a big impact on separation performance, but as a consequence of improper tooling analysts currently do not consider the whole parameter space. We propose to solve these problems by applying visual analytics (VA) principles. To this end, we developed a task abstraction and visualization design in a user-centered design process. We present TBSSvis, an interactive webbased VA prototype, which we evaluated in two qualitative user studies. Feedback and observations from these studies show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBBS results. It also provides guidance to facilitate informed parameter selection and the analysis of the data at hand.