In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies.
We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cram\'er-Rao theory.
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data.
We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted patterns.
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.
Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity.
With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data that need to be jointly analyzed to explore their latent connections.
Thus, we provide the additional conditions for when the arbitrary ordering of the sources within each dataset is common.