Spectral signal space projection algorithm for frequency domain MEG and EEG denoising, whitening, and source imaging

MEG and EEG data contain additive correlated noise generated by environmental and physiological sources. To suppress this type of spatially coloured noise, source estimation is often performed with spatial whitening based on a measured or estimated noise covariance matrix. However, artifacts that span relatively small noise subspaces, such as cardiac, ocular, and muscle artifacts, are often explicitly removed by a variety of denoising methods (e.g., signal space projection) before source imaging. Here, we introduce a new approach, the spectral signal space projection (S3P) algorithm, in which time–frequency (TF)-specific spatial projectors are designed and applied to the noisy TF-transformed data, and whitened source estimation is performed in the TF domain. The approach can be used to derive spectral variants of all linear time domain whitened source estimation algorithms. The denoised sensor and source time series are obtained by the corresponding inverse TF-transform. The method is evaluated and compared with existing subspace projection and signal separation techniques using experimental data. Altogether, S3P provides an expanded framework for MEG/EEG data denoising and whitened source imaging in both the time and frequency/scale domains.

Research Highlights
► MEG and EEG data are corrupted by frequency-specific (FS) spatially coloured noise.
► S3P suppresses this noise with FS spatial projections and whitened source imaging.
► S3P performs better than other methods because the noise spatial patterns are FS.
► S3P provides a new expanded framework for imaging denoised brain oscillations.