Automated non-ocular artefact removal (ANOAR) 2016, Jevri Hanna Module works with MNE Python. Globally bad channels are removed by calculating a distance-weighted absolute correlation matrix, and identifying those that correlate poorly with their neighbours. As for artefact rejection, the sum of squared deviance from the ERP average is calculated for each trial, individually for each channel. This results in a matrix of noise measurements, TxC, where T is the number of trials, and C is the number of channels. Noise levels laying outside a given threshold of a probability distribution are marked as bad. Because the values describe both trial and channel, individual channels can be marked as bad without throwing out the entire trial. The values of these channels are replaced by interpolation within the trial alone. Trials which have too many bad channels are marked as bad entirely. The critical manipulation here that prevents the removal of ocular artefacts is that the correlation with the EOG channels for each trial-channel are calculated, and the noise value for that trial-channel is multiplied by 1-r². This in effect removes the portion of the noise caused by parts of the signal that correlate with EOG (or any other recorded source of noise, EKG, etc).