Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

Abstract

A beamformer enhances the signal from a voxel of interest by minimising interference from all other locations represented in the sensor covariance matrix. However, the presence of narrowband oscillations in EEG/MEG implies that the spatial structure of the covariance matrix, and hence also the optimal beamformer, depends on the frequency. The frequency-adaptive broadband (FAB) beamformer introduced here exploits this fact in the Fourier domain by partitioning the covariance matrix into cross-spectra corresponding to different frequencies. For each frequency bin, an individual spatial filter is constructed. This assures optimal noise suppression across the frequency spectrum. After applying the spatial filters in the frequency domain, the broadband source signal is recovered using the inverse Fourier transform. MEG simulations using artificial data and real resting-state measurements were used to compare the FAB beamformer to the LCMV beamformer and MNE. The FAB beamformer significantly outperforms both methods in terms of the quality of the reconstructed time series. To our knowledge, the FAB beamformer is the first beamforming approach tailored for the analysis of broadband neuroimaging data. Due to its frequency-adaptive noise suppression, the reconstructed source time series is suited for further time-frequency or connectivity analysis in source space.