Source localization of EEG/MEG data by correlating columns of ICA and lead field matrices

Independent components analysis (ICA) has previously been used to denoise EEG/magnetoencephalography (MEG) signals before performing neural source localization. Source localization is then performed using a method such as beamforming or dipole fitting. Here we show how ICA can also be used as a source localization method, negating the need for beamforming and dipole fitting. This type of approach is valid whenever an estimate of the forward (mixing) model for all putative source locations is available, which includes EEG and MEG applications. The proposed method consists of estimating the forward model using the laws of physics, estimating a second forward model using ICA, and then correlating the columns of the matrices that represent the two forward models. We show that, when synthetic data are used, the proposed localization method produces a smaller localization error than several alternatives. We also show localization results for real auditory-evoked MEG data.