Independent component analysis is a method of blind source separation that separates N linear mixtures into N independent informational components (Makeig et al., 1997). It is based on the assumption that source signals are statistically independent whereas signal mixtures are not. Maximizing the joint entropy of the extracted signals gives rise to the “un-mixing matrix” W that, when multiplied by EEG data X, produces the original source signals U, i.e., U = WX. The columns of the un-mixing matrix, W, hold coefficients of spatial filters that pass the activity of only one independent source process and suppress all the others. Each IC is represented by the time-course of activation (given by each row of U), and the weights with which the component projects to the electrodes which are given in the inverse of the un-mixing matrix W−1. Plotting these weights onto a schematic head model allows one to visualize the scalp topography of each independent component.
