# What are the proper pre-processing steps to perform Independent Component Analysis?

What are the proper steps for preprocessing my waveforms in order to perform an independent component analysis (ICA) later? I understand the how, though further explanation of that doesn't hurt, but I'm more interested in the why.

• I'm not sure why you need preprocessing. Is there any particular reason? Commented Aug 19, 2011 at 20:45
• @Phonon I've encountered investigators that have sphered their data before performing ICA on it. I just wondered if there was a standard method. Commented Aug 19, 2011 at 20:46
• Very interesting. I'd love to see a constructive answer. Commented Aug 19, 2011 at 20:48
• In the case of spectral analysis on EEG signals, people whiten to reduce the dominating effect of the ${1}/{f}$ shape of the spectrum, which often hides interesting things at high frequencies. There's at least a little discussion of this here in the supplemental materials. Whether this is a common trick before ICA in particular, not sure. Is your application EEG/MEG/LFP signals? Maybe someone who does ICA can flesh this out into a full answer, if my hunch is right. Interesting question - I'll read up on it. Commented Aug 20, 2011 at 0:13
• @Gigili That's part of the question, too. Which are the ones considered to be the normal steps? Commented Aug 20, 2011 at 23:24

Independent component analysis (ICA) is used to separate a linear mixture of statistically independent and most importantly, non-Gaussian components into its constituents. The standard model for a noise-free ICA is

$$\mathbf{x}=\mathbf{As}$$

where $\mathbf{x}$ is the observation or data vector, $\mathbf{s}$ is a source signal/original components (non-Gaussian) and $\mathbf{A}$ is a transformation vector that defines the linear mixing of the constituent signals. Typically, $\mathbf{A}$ and $\mathbf{s}$ are unknown.

### Pre-processing

There are two main pre-processing strategies in ICA, namely centering and whitening/sphering. The primary reasons for pre-processing are:

• Simplification of algorithms
• Reduction of dimensionality of the problem
• Reduction of number of parameters to be estimated.
• Highlighting features of the data set not readily explained by the mean and covariance.

From the introduction of G. Li and J. Zhang, "Sphering and its properties", The Indian Journal of Statistics, Vol. 60, Series A, Part I, pp. 119-133, 1998:

Outliers, clusters or other kind of groups, and concentrations near curves or non-ﬂat surfaces are probably the important features that interest data analysts. They are, in general, not obtainable through mere knowledge of the sample mean and covariance matrix. In these circumstances, it is desirable to separate oﬀ the information contained in the mean and the covariance matrices and forces us to examine aspects of our data sets other than those well-understood natures. Centering and sphering is a simple and intuitive approach that eliminates the mean-covariance information and helps to highlight structures beyond linear correlation and elliptic shapes, and therefore is often performed before exploring displays or analyses of data sets

1. Centering:

Centering is a very simple operation and simply refers to subtracting the mean $\mathbb{E}\{\mathbf{x}\}$. In practice, you use the sample mean and create a new vector $\mathbf{x}_c=\mathbf{x}-\overline{\mathbf{x}}$, where $\overline{\mathbf{x}}$ is the mean of the data. Geometrically, subtracting the mean is equivalent to translating the center of coordinates to the origin. The mean can always be re-added to the result the end (this is possible because matrix multiplication is distributive).

2. Whitening:

Whitening is a transformation that converts the data such that it has an identity covariance matrix, i.e., $\mathbb{E}\{\mathbf{x}_c\mathbf{x}_c^T\}=\mathbf{I}$. Normally, you work with the sample covariance matrix,

$$\widehat{\mathbf{\Sigma}}=C.\mathbf{x}_c\mathbf{x}_c^T$$

where $C$ is just my lazy placeholder for the appropriate normalization factor (depending on the dimensions of $\mathbf{x}$). A new whitened vector is created as

$$\mathbf{x}_w=\widehat{\mathbf{\Sigma}}^{-1/2}\mathbf{x}_c$$

which will have a covariance of $\mathbf{I}$. Geometrically, whitening is a scaling transformation. Here is a small example in Mathematica:

s = RandomReal[{-1, 1}, {2000, 2}];
A = {{2, 3}, {4, 2}};
x = s.A;
whiteningMatrix = Inverse@CholeskyDecomposition[[email protected]/Length@x];
y = x.whiteningMatrix;
FullGraphics@GraphicsRow[
ListPlot[#, AspectRatio -> 1, Frame -> True] & /@ {s, x, y}]


The first plot is the joint density of two uniformly distributed random vectors, or the components $\mathbf{s}$. The second shows the effect of multiplying by a transformation vector $\mathbf{A}$. The square gets skewed and scaled into a rhombus. By multiplying with the whitening matrix, the joint density is back to a square which is slightly rotated from the original.

Because of the whitening transformation, in the new system that is being solved, i.e. $\mathbf{x}_w=\mathbf{A}_w\mathbf{s}_w$, $\mathbf{A}_w$ is an orthogonal matrix. This can be easily shown:

\begin{align} \mathbb{E}\{\mathbf{x}_w\mathbf{x}_w^T\}&=\mathbb{E}\{\mathbf{A}_w\mathbf{s}_w(\mathbf{A}_w\mathbf{s}_w)^T\}\\ &=\mathbf{A}_w\mathbb{E}\{\mathbf{s}_w\mathbf{s}_w^T\}\mathbf{A}_w^T\\ &=\mathbf{A}_w\mathbf{A}_w^T=\mathbf{I} \end{align}

where the last step follows because of the statistical independence of $\mathbf{s}_i$ The orthogonality condition means that there are only about half as many parameters that need to be estimated. (Note: Although this is true in this case and in my example, $\mathbf{A}$ need not be square to begin with).

If, after the transformation, there are eigenvalues close to zero, then these can be safely discarded as they are just noise and will only hamper the estimation due to "overlearning".

3. Other pre-processing

There might be other pre-processing steps involved in certain specific applications that are impossible to cover in an answer. For example, I've seen a few articles which use the log of the time-series and a few others that filter the time-series. While it might be suited for their particular application/conditions, the results don't carry over to all fields.

I believe it is possible to use ICA if at most one of the components is Gaussian, although I can't find a reference for this right now.

### Why is it called "sphering"?

This is probably well known, but just as a fun fact, sphering comes from the change in the structure of covariance matrices in the case of Gaussian components from an $n$-dimensional hyper ellipsoid to an $n$-dimensional sphere due to whitening. Here's an example (use the same code as above, but replace {-1,1} with NormalDistribution[])

The first is the joint density for two uncorrelated Gaussians, the second under transformation and the third is after whitening. In practice only steps 2 and 3 are visible.