Tzyy-Ping Jung, Colin Humphries, Te-Won Lee, Scott Makeig, Martin McKeown, Vicente Iragui, Terrence J. Sejnowski
Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rejecting contami(cid:173) nated EEG segments results in a considerable loss of information and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG records also contain brain signals [1, 2], so regressing out EOG ac(cid:173) tivity inevitably involves subtracting a portion of the relevant EEG signal from each recording as well. Regression cannot be used to remove muscle noise or line noise, since these have no reference channels. Here , we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records. The method is based on an extended version of a previous Indepen(cid:173) dent Component Analysis (lCA) algorithm [3, 4] for performing blind source separation on linear mixtures of independent source signals with either sub-Gaussian or super-Gaussian distributions. Our results show that ICA can effectively detect, separate and re(cid:173) move activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods.
Extended leA Removes Artifacts from EEG Recordings