Group picture taken at TDA2016
TDA 2016 (Taken by Pieter Kroonenberg)

The quest for a general functional tensor framework for blind source separation

Our overall objective is the development of a general functional framework for solving tensor based blind source separation (BSS) problems in biomedical data fusion, using tensor decompositions (TDs) as basic core. We claim that TDs will allow the extraction of fairly complicated sources of biomedical activity from fairly complicated sets of uni- and multimodal data. The power of the new techniques will be demonstrated for three well-chosen representative biomedical applications for which extensive expertise and fully validated datasets are available in the PI’s team, namely:

Solving these challenging problems requires that algorithmic progress is made in several directions:

The algorithms are eventually integrated in an easy-to-use open source software platform that is general enough for use in other BSS applications.

Having been involved in biomedical signal processing over a period of 20 years, the PI has a good overview of the field and the opportunities. By working directly at the forefront in close collaboration with the clinical scientists who actually use our software, we can have a huge impact.