EEG source localization using beamforming in energy-constrained regions
David Gutiérrez, C. C. Zaragoza-Martinez · 2012
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.