Biomedical Engineering Reference
In-Depth Information
FIGURE 3.5:
Comparison of DTF (panel B) and PDC (panel D) for simulation
scheme A. Resulting schemes of propagation for DTF and PDC below corresponding
panels. Thickness of arrows proportional to the flow intensities. The convention of
presenting DTF/PDC is as in
Figure 3.4.
Note that the weak flow from channel 5 is
enhanced by PDC.
3.6
Multivariate signal decompositions
3.6.1
Principal component analysis (PCA)
3.6.1.1
Definition
Principal component analysis (PCA) is a method of decomposition of multichan-
nel epoched data into components that are linearly independent; that is, spatially and
temporally uncorrelated. Depending on the field of application, it is also named the
discrete Karhunen-Loeve transform (KLT), the Hotelling transform, or proper or-
thogonal decomposition (POD). The geometrical intuition behind this method is the
following. We treat samples from all channels at a given time moment as a point in
the space of dimension equal to the number of channels. One epoch of multichannel
data forms a cloud of points in that space. This cloud can be spread more in some
directions than in the others. The measure of the spread is the variance of the points'
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