  Multivariate or multichannel data have become ubiquitous in many modern
scientific and engineering applications, e.g., biomedical engineering, owing to
recent advances in sensor and computing technology. Processing these data sets
is challenging owing to: i) their large size and multidimensional nature, thus
requiring specialized algorithms and efficient hardware designs for on-line and
real-time processing; ii) the nonstationary nature of data arising in many real
life applications demanding new extensions of standard multiscale
non-stationary signal processing tools. In this paper, we address the former
issue by proposing a fully FPGA based hardware architecture of a popular
multi-scale and multivariate signal processing algorithm, termed as
multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method
that extends the functionality of standard empirical mode decomposition (EMD)
algorithm to multichannel or multivariate data sets. Since its inception in
2010, the algorithm has found wide spread applications spanning different
engineering related fields. Yet, no parallel FPGA based hardware design of the
algorithm is available for its on-line and real-time processing. Our proposed
architecture for MEMD uses fixed point operations and employs cubic spline
interpolation within the sifting process. Finally, examples of decomposition of
multivariate synthetic and real world biological signals are provided.
