  In this paper, we formulate the Canonical Correlation Analysis (CCA) problem
on matrix manifolds. This framework provides a natural way for dealing with
matrix constraints and tools for building efficient algorithms even in an
adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to
a change detection problem in EEG signals.
