The basic idea of SOUND is simple. The noise level of each channel is evaluated iteratively by comparing the trace of the channel of interest against the recordings of all the other channels. SOUND uses minimum-norm estimation (MNE) to find the most likely cortical current distribution given the recordings of the other channels. From the obtained MNE, SOUND estimates the most likely signal in the channel of interest. Depending on the discrepancy between the estimated and the actual recording in the channel of interest, the noise level of the analysed channel is set. After the noise level in one channel has been evaluated, SOUND continues to estimate the noise level in the remaining channels in an identical way. As the iteration proceeds, the noise estimates, and thus, also the MNEs become increasingly precise. Once SOUND has converged to the final channel noise-level estimates, SOUND computes the final noise-suppressed MNE using all the channels simultaneously. From this noise-suppressed MNE, the final cleaned versions of the channel traces are calculated.