Extended ICA Removes Artifacts from Electroencephalographic Recordings

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 contaminated 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 activity 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 Independent Component Analysis (ICA) 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 remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods.

[1]  D. Overton,et al.  Distribution of eye movement and eyeblink potentials over the scalp. , 1969, Electroencephalography and clinical neurophysiology.

[2]  S. Hillyard,et al.  Eye movement artifact in the CNV. , 1970, Electroencephalography and clinical neurophysiology.

[3]  P. Lang,et al.  The effects of eye fixation and stimulus and response location on the contingent negative variation (CNV). , 1973, Biological psychology.

[4]  H. Moldofsky,et al.  A spectral method for removing eye movement artifacts from the EEG. , 1978, Electroencephalography and clinical neurophysiology.

[5]  T. Gasser,et al.  Correction of EOG artifacts in event-related potentials of the EEG: aspects of reliability and validity. , 1982, Psychophysiology.

[6]  J. C. Woestenburg,et al.  The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain , 1983, Biological Psychology.

[7]  J. Kenemans,et al.  Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data. , 1991, Psychophysiology.

[8]  P. Berg,et al.  Dipole models of eye movements and blinks. , 1991, Electroencephalography and clinical neurophysiology.

[9]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[10]  A. J. Bell,et al.  Fast blind separation based on information theory , 1995 .

[11]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[12]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[13]  C. Fyfe,et al.  Generalised independent component analysis through unsupervised learning with emergent Bussgang properties , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).