EEG source localization using beamforming in energy-constrained regions

We present a source localization method for electroencephalographic (EEG) data based on the linearly constrained minimum variance and eigencanceler beamformers. A region-of-interest (ROI) is selected through a short-term estimate of the signal´s energy as constraint. Such constraint is only valid on the scalp, then an affine transformation is applied to map it to the brain cortex. After this process, the beamforming-based source localization is applied only within the ROI, which allows for a reduction in the computational cost compared to an exhaustive search on the whole brain cortex. The applicability of the proposed method is shown through a series of numerical examples using real EEG data. Our results show that the eigencanceler offers a more focused and less biased source estimate in comparison to the one based on the classical LCMV beamformer.