Automated Artifact Removal From the Electroencephalogram

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[2]  C. Joyce,et al.  Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. , 2004, Psychophysiology.

[3]  G. W. Milligan,et al.  A monte carlo study of thirty internal criterion measures for cluster analysis , 1981 .

[4]  Qinyu Zhang,et al.  Neural network based EEG denoising , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Andreas Ziehe,et al.  TDSEP — an efficient algorithm for blind separation using time structure , 1998 .

[6]  Slawomir J. Nasuto,et al.  Single-trial event-related potential analysis for brain-computer interfaces , 2008 .

[7]  Mosa'ad A. Al-Abdulmunem,et al.  Spontaneous blink rate of a normal population sample , 1999 .

[8]  D.J. McFarland,et al.  The wadsworth BCI research and development program: at home with BCI , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Eytan Domany,et al.  Resampling Method for Unsupervised Estimation of Cluster Validity , 2001, Neural Computation.

[10]  P. Senthil Kumar,et al.  Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel , 2008 .

[11]  R. Kass,et al.  Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods , 2004 .

[12]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[13]  Inderjit S. Dhillon,et al.  Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..

[14]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  R. Barry,et al.  Removal of ocular artifact from the EEG: a review , 2000, Neurophysiologie Clinique/Clinical Neurophysiology.

[16]  Masatake Akutagawa,et al.  The removal of EMG in EEG by neural networks. , 2010, Physiological measurement.

[17]  Richard J. Davidson,et al.  Electromyogenic artifacts and electroencephalographic inferences revisited , 2011, NeuroImage.

[18]  R. Mojena,et al.  Hierarchical Grouping Methods and Stopping Rules: An Evaluation , 1977, Comput. J..

[19]  Ana Maria Tomé,et al.  Removal of ocular artefacts from electroencephalograms using singular spectrum analysis , 2005 .

[20]  Wolfgang Rosenstiel,et al.  Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation , 2007, Comput. Intell. Neurosci..

[21]  A. Varri,et al.  The SIESTA project polygraphic and clinical database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[22]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[23]  Slawomir J. Nasuto,et al.  Automatic Artefact Removal from Event-related Potentials via Clustering , 2007, J. VLSI Signal Process..

[24]  Terrence J. Sejnowski,et al.  Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis , 2007, NeuroImage.

[25]  Christian Sander,et al.  ICA-based muscle artefact correction of EEG data: What is muscle and what is brain? Comment on McMenamin et al. , 2011, NeuroImage.

[26]  R. Kass,et al.  Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[27]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  S P Fitzgibbon,et al.  Removal of EEG Noise and Artifact Using Blind Source Separation , 2007, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[29]  Richard J. Davidson,et al.  Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG , 2010, NeuroImage.

[30]  J. Wolpaw,et al.  EMG contamination of EEG: spectral and topographical characteristics , 2003, Clinical Neurophysiology.

[31]  Vera Kaiser,et al.  What does clean EEG look like? , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Patrick Berg,et al.  Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies , 2002, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.