Removing electroencephalographic artifacts by blind source separation

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise pr esent serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptab le data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often r egression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing th e appearance and spread of EOG artifacts in the EEG channels. Because EEG a nd ocular activity mix bidirectionally regressing out eye artifacts inevita bly involves subtracting relevant EEG signals from each record as well. Reg ression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifa cts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separat e eye artifacts from brain signals, especially when they have comparable am plitudes. Here, we propose a new and generally applicable method for removi ng a wide variety of artifacts from EEG records based on blind source separ ation by independent component analysis (ICA). Our results on EEG data coll ected from normal and autistic subjects show that ICA can effectively detec t, separate, and remove contamination from a wide variety of artifactual so urces in EEG records with results comparing favorably with those obtained u sing regression and PCA methods. ICA can also be used to analyze blink-rela ted brain activity.