# Covariance (Noise Regularization)

## Covariance (Noise Regularization)

Two methods to estimate the channel noise correlation matrix CN are provided by BESA Research: Use baseline or Use 15%

lowest values. In each case, the activity (noise or signal, respectively) is defined as root-mean-square across all respective

latencies for each channel. The noise covariance matrix is constructed as a diagonal matrix. The entries in the main diagonal

are proportional to the noise activity of the individual channels (if selected) or are all equally proportional to the average noise

activity over all channels. The noise covariance matrix CN is then scaled such that the ratio of the Frobenius norms of the weighted

leadfield projector matrix (LRLT) and the noise covariance matrix CN equals the Signal-to-Noise ratio. This scaling can be

multiplied by an additional factor (default=1) to sharpen (<1) or smoothen (>1) the minimum norm image.

The figure shows a minimum norm image computed from the file Examples\Epilepsy\Spikes\Spikes-Child4_EEG+MEG_averaged.fsg.