First a little bit of background.
I'm interested in exploring the performance of Independent Component Analysis (ICA) in the context of disentangling intracranial EEG signals. These signals are measures of voltage along time at different points in space. If we understand intracranial EEG as a mixture of different signals generated by different neural elements, say, different neuronal populations, ¿would be possible to recover these components (representing different neuronal elements) from the mixture using ICA? At the core of this idea is the assumption that components are statistically independents, maybe pretty unrealistic since a natural consequence of neuronal connectivity is the correlated neuronal activity.
So, my question is. I have some benchmark signals that have been generated in a realistic computational model of the cerebral cortex microcircuit. Each signal is a temporal series representing the activity of a neuronal population. Before mixing the signals and apply ICA, I would like test about the independence assumption. Then, I'm looking for methods (and software) to test the independence between these signals: measures of mutual information, linear and non-linear correlations, whatever (I suppose). Any way to support or reject the assumption of independence.
I'm sorry for the lack of mathematical formality, and maybe accuracy, in the question. I'm not an expert. Feel free to correct me if I've made an error above. Thank you in advance!