Voxel-ICA for reconstruction of source signal time-series and orientation in EEG and MEG

In electroencephalography (EEG) and magnetoencephalography signal processing, scalar beamformers are a popular technique for reconstruction of the time-course of a brain source in a single time-series. A prerequisite for scalar beamformers, however, is that the orientation of the source must be known or estimated, whereas in reality the orientation of a brain source is often not known in advance and current techniques for estimation of brain source orientation are effective only for high signal-to-noise ratio (SNR) brain sources. As a result, vector beamformers are applied which do not need the orientation of the source and reconstruct the source time-course in three orthogonal (x, y, and z) directions. To obtain a single time-course, the vector magnitude of the three orthogonal outputs of the beamformer can be calculated at each time point (often called neural activity index, NAI). The NAI, however, is different from the actual time-course of a source since it contains only positive values. Moreover, in estimating the magnitude of the desired source, the background activity (undesired signals) in the beamformer outputs also become all positive values, which, when added to each other, leads to a drop in the SNR. This becomes a serious problem when the desired source is weak. We propose applying independent component analysis (ICA) to the orthogonal time-courses of a brain voxel, as reconstructed by a vector beamformer, to reconstruct the time-course of a desired source in a single time-series. This approach also provides a good estimation of dipole orientation. Simulated and real EEG data were used to demonstrate the performance of voxel-ICA and were compared with a scalar beamformer and the magnitude time-series of a vector beamformer. This approach is especially helpful when the desired source is weak and the orientation of the source cannot be estimated by other means.