These and many other cases make it clear that there is a need for relaxing the independence assumption and generalising the methods developed for independent source separation to the case of statistically dependent sources but with some other additional structure in the source space.
Independent component analysis (ICA) is a blind source separation method [43] that consists of decomposing the EEG time series, which consists of a linear mixture of signals from many sources, into a set of statistically independent signals called independent components (ICs) [44] prior to any dipole fitting procedure.
On the other side, the main source of energy was also statistically significant.
Meta-regression was performed for both time of exposure and source of controls and no statistically significant associations were found.
Meta-regression was performed for both timing of exposure and source of controls and no statistically significant associations were found.
Subgroup analyses of the trials according to risk of bias, length of study, and source of funding showed no statistically significant differences between subgroups.
Subgroup analyses of the trials according to risk of bias, length of study, diagnostic criteria for type 2 diabetes, and source of funding showed no statistically significant differences between subgroups (that is, no significant interaction).
This is typical, for example, for contemporary predictive processing frameworks (Hohwy 2013; Clark 2013), in which a model of the future state of the system is built, and then the model is compared with that state, when it occurs; from a technical point of view, the model becomes another source of information, though not statistically independent.
It is well known that a blind separation of statistically independent sources of convolutive mixture can lead us to the original sources up to a permutation and scalar filter: ŝ 1 ( n ) = h 1 ( z ) ∗ s 1 ( n ) + h 2 ( z ) ∗ s 2 ( n ) (16).
The advantage of ICA resides in the un-mixing of statistically independent sources in an automated, and statistically unbiased fashion.
