**2.4. Independent Component Analysis**

Independent Component Analysis (ICA) is an approach where the objective is to find a linear representation of non-gaussian data and the calculated components are statistically independent [19]. In literature at least three definitions of ICA has been given [20-22]:


The first definition is the most general one, as no a priori assumptions on the data are made. However it is an imprecise definition, as it is necessary to define a measure of independence for *si.* The second definition reduces the ICA problem to an estimation of a latent variable method, but this estimate can be quite difficult; definition 3 is actually the most used one.

The possibility to identify a noise-free ICA approach is ensured by adding the following assumptions [22]:


ICA can be used to extract features finding independent directions in the input space. This objective is more difficult than using PCA approach, as in PCA the variance of data along a direction can be immediately calculated and it is maximised by PCA itself, while there is not straightforward metric for quantifying the independence of directions belonging to the input space [23]. Recently, in order to extract independent components, neural network algorithms have been adopted [24].
