  Temporal Blind Source Separation (TBSS) is used to obtain the true underlying
processes from noisy temporal multivariate data, such as electrocardiograms.
TBSS has similarities to Principal Component Analysis (PCA) as it separates the
input data into univariate components and is applicable to suitable datasets
from various domains, such as medicine, finance, or civil engineering. Despite
TBSS's broad applicability, the involved tasks are not well supported in
current tools, which offer only text-based interactions and single static
images. Analysts are limited in analyzing and comparing obtained results, which
consist of diverse data such as matrices and sets of time series. Additionally,
parameter settings 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. Our primary contribution is a design study for TBSS,
which so far has not been explored by the visualization community. We developed
a task abstraction and visualization design in a user-centered design process.
Task-specific assembling of well-established visualization techniques and
algorithms to gain insights in the TBSS processes is our secondary
contribution. We present TBSSvis, an interactive web-based VA prototype, which
we evaluated extensively in two interviews with five TBSS experts. Feedback and
observations from these interviews show that TBSSvis supports the actual
workflow and combination of interactive visualizations that facilitate the
tasks involved in analyzing TBSS results.
