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Statistical control of artifacts in dense array EEG/MEG studies
Author(s) -
Junghöfer Markus,
Elbert Thomas,
Tucker Don M.,
Rockstroh Brigitte
Publication year - 2000
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/1469-8986.3740523
Subject(s) - artifact (error) , magnetoencephalography , computer science , electroencephalography , channel (broadcasting) , pattern recognition (psychology) , artificial intelligence , interpolation (computer graphics) , signal (programming language) , psychology , image (mathematics) , computer network , programming language , psychiatry
With the advent of dense sensor arrays (64–256 channels) in electroencephalography and magnetoencephalography studies, the probability increases that some recording channels are contaminated by artifact. If all channels are required to be artifact free, the number of acceptable trials may be unacceptably low. Precise artifact screening is necessary for accurate spatial mapping, for current density measures, for source analysis, and for accurate temporal analysis based on single‐trial methods. Precise screening presents a number of problems given the large datasets. We propose a procedure for statistical correction of artifacts in dense array studies (SCADS), which (1) detects individual channel artifacts using the recording reference, (2) detects 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 across trials to document the stability of the averaged waveform. Examples from 128‐channel recordings and from numerical simulations illustrate the importance of careful artifact review in the avoidance of analysis errors.