How to normalize a signal to zero mean and unit variance?

Solution 1:

if your signal is in the matrix X, you make it zero-mean by removing the average:

X=X-mean(X(:));

and unit variance by dividing by the standard deviation:

X=X/std(X(:));

Solution 2:

If you have the stats toolbox, then you can compute

Z = zscore(S);

Solution 3:

You can determine the mean of the signal, and just subtract that value from all the entries. That will give you a zero mean result.

To get unit variance, determine the standard deviation of the signal, and divide all entries by that value.

Solution 4:

It seems like you are essentially looking into computing the z-score or standard score of your data, which is calculated through the formula: z = (x-mean(x))/std(x)

This should work:

%% Original data (Normal with mean 1 and standard deviation 2)
x = 1 + 2*randn(100,1);
mean(x)
var(x)
std(x)

%% Normalized data with mean 0 and variance 1
z = (x-mean(x))/std(x);
mean(z)
var(z)
std(z)