TBSSvis: Visual Analytics for Temporal Blind Source Separation
Nikolaus Piccolotto, Markus B\"ogl, Theresia Gschwandtner, Christoph, Muehlmann, Klaus Nordhausen, Peter Filzmoser, Silvia Miksch

TL;DR
TBSSvis is a visual analytics tool designed to enhance the analysis of Temporal Blind Source Separation results, addressing current tool limitations by enabling interactive exploration and parameter space consideration.
Contribution
This paper presents a novel visualization design study and a web-based prototype for TBSS, improving analysis capabilities in a domain lacking dedicated visual tools.
Findings
Supports TBSS workflow with interactive visualizations
Enables exploration of parameter effects on separation quality
Validated through expert interviews with positive feedback
Abstract
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…
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Taxonomy
TopicsBlind Source Separation Techniques · Sensory Analysis and Statistical Methods
