ICA makes the following critical assumptions:
- The sources of the signals are statistically independent
- Linear transformations are sufficient to capture the relevant information
- The independent components do not have a normal distribution
- The mixing matrix A can be inverted
ICA also requires the data to be centered and whitened; that is, to be mutually uncorrelated with unit variance. Preprocessing the data using PCA as outlined above achieves the required transformations.