MultiView Independent Component Analysis with Delays
Ambroise Heurtebise Pierre Ablin Alexandre Gramfort
Linear Independent Component Analysis (ICA) is a blind source separation
technique that has been used in various domains to identify independent latent
sources from observed signals. In order to obtain a higher signal-to-noise
ratio, the presence of multiple views of the same sources can be used. In this
work, we present MultiView Independent Component Analysis with Delays (MVICAD).
This algorithm builds on the MultiView ICA model by allowing sources to be
delayed versions of some shared sources: sources are shared across views up to
some unknown latencies that are view- and source-specific. Using simulations,
we demonstrate that MVICAD leads to better unmixing of the sources. Moreover,
as ICA is often used in neuroscience, we show that latencies are age-related
when applied to Cam-CAN, a large-scale magnetoencephalography (MEG) dataset.
These results demonstrate that the MVICAD model can reveal rich effects on
neural signals without human supervision.