Removing electroencephalographic artifacts by blind source separation.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

[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]  E Donchin,et al.  A new method for off-line removal of ocular artifact. , 1983, Electroencephalography and clinical neurophysiology.

[8]  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.

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

[10]  J. Nadal Non linear neurons in the low noise limit : a factorial code maximizes information transferJean , 1994 .

[11]  J. Nadal,et al.  Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[12]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[13]  Andrzej Cichocki,et al.  Robust learning algorithm for blind separation of signals , 1994 .

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

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

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

[17]  Yoram Baram,et al.  Multidimensional density shaping by sigmoids , 1996, IEEE Trans. Neural Networks.

[18]  Barak A. Pearlmutter,et al.  Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA , 1996, NIPS.

[19]  A. J. Bell,et al.  Blind Separation of Event-Related Brain Responses into Independent Components , 1996 .

[20]  Dinh-Tuan Pham,et al.  Blind separation of instantaneous mixture of sources via an independent component analysis , 1996, IEEE Trans. Signal Process..

[21]  R. Lambert Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures , 1996 .

[22]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[23]  Ehud Weinstein,et al.  Multichannel signal separation: methods and analysis , 1996, IEEE Trans. Signal Process..

[24]  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).

[25]  Erkki Oja,et al.  A class of neural networks for independent component analysis , 1997, IEEE Trans. Neural Networks.

[26]  Shun-ichi Amari,et al.  Stability Analysis Of Adaptive Blind Source Separation , 1997 .

[27]  T. Lagerlund,et al.  Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[28]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic and Event-Related Potential Data , 1998 .

[29]  Tzyy-Ping Jung,et al.  Analyzing and Visualizing Single-Trial Event-Related Potentials , 1998, NIPS.

[30]  Mark A. Girolami,et al.  An Alternative Perspective on Adaptive Independent Component Analysis Algorithms , 1998, Neural Computation.

[31]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..

[32]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[33]  Terrence J. Sejnowski,et al.  Independent Component Analysis of Simulated ERP Data , 2000 .

[34]  Thomas P. Flanders,et al.  Performing Organization Name(s) and Address(es) , 2001 .

[35]  B. AfeArd CALCULATING THE SINGULAR VALUES AND PSEUDOINVERSE OF A MATRIX , 2022 .