Statistical control of artifacts in dense array EEG/MEG studies

With the advent of dense sensor arrays (64-256 channels) in electroencephal ography and magnetoencephalography studies, the probability increases that some recording channels are contaminated by artifact. If all channels are r equired to be artifact free, the number of acceptable trials may be unaccep tably low. Precise artifact screening is necessary for accurate spatial map ping, for current density measures, for source analysis, and for accurate t emporal analysis based on single-trial methods. Precise screening presents a number of problems given the large datasets. We propose a procedure for s tatistical correction of artifacts in dense array studies (SCADS), which (1 ) detects individual channel artifacts using the recording reference, (2) d etects global artifacts using the average reference, (3) replaces artifact- contaminated sensors with spherical interpolation statistically weighted on the basis of all sensors, and (4) computes the variance of the signal acro ss trials to document the stability of the averaged waveform. Examples from 128-channel recordings and from numerical simulations illustrate the impor tance of careful artifact review in the avoidance of analysis errors.