Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

Objectives: Electrical potentials produced by blinks and eye movements pres ent serious problems for electroencephalographic (EEG) and event-related po tential (ERP) data interpretation and analysis, particularly for analysis o f data from some clinical populations. Often, all epochs contaminated by la rge eye artifacts are rejected as unusable, though this may prove unaccepta ble when blinks and eye movements occur frequently. Methods: Frontal channels are often used as reference signals to regress ou t eye artifacts, but inevitably portions of relevant EEG signals also appea ring in EOG channels are thereby eliminated or mixed into other scalp chann els. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2 103; Neural Computation 11(2) (1999) 606) overcomes these limitations. Results: Results on EEG data collected from 28 normal controls and 22 clini cal subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from e ven strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods. Conclusions: The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the s ingle trials are artifact-free. (C) 2000 Elsevier Science ireland Ltd. All rights reserved.