Separation/extraction of sources are wide concepts in information sciences, since sensors provide signal mixing and an essential step consists in separating/extracting useful information from unuseful one, the noise. The project addresses three challenges.
In many areas like brain imaging, hyperspectral imaging, due to various kinds of sensors, there are many ways for recording the same physical phenomenon leading to sets of multimodal data. Multimodality has been studied in human-computer interface or in data fusion, but never at the signal level. The first challenge is to provide a general framework of multimodal source signals.
There exist a few cases where the mixtures are essentially nonlinear, e.g. with chemical sensors. However, up to now, most of the source separation/extraction results and methods are restricted to linear mixtures. The second challenge is to enlarge theoretical results on identifiability and algorithms in nonlinear source separation, especially for new classes of nonlinearities (e.g. multilinear) and priors on sources.
In high-dimension data (e.g. EEG or MRI in brain imaging), separating all the sources is neither tractable nor relevant, and one would like to only extract the useful sources. Conversely, for a small number of sensors, especially smaller than the number of sources, it is again necessary to only focus on the useful signals. The third challenge is to develop generic framework for only extracting useful signals, based on coarse reference signals or priors.
Finally, validation and relevant modeling will be based on actual signals and problems, especially in biomedical engineering (brain-computer interface, EEG, fMRI), chemical engineering, audio-visual scene analysis and hyperspectral imaging.
Breakdown results are waited in methodology, with an enhanced general framework in source separation for multimodal, multidimensional and nonlinear signals, which should have high societal impacts in many domains like health and environment.