Classification of Artifactual ICA Components

The analysis of EEG signals for the use in BCI systems and for mental state monitoring applications is often impeded by artifacts caused by muscular activity or external technical sources. A promising approach for the reduction or removal of artifacts is based on methods of Blind Source Separation (BSS), which transform the original EEG signal into independent source components. In order to avoid the time-consuming hand rating of sources into artifactual and non-artifactual components, an automated method for their classification is proposed. Applying state of the art machine learning algorithms and nonlinear classification with a Support Vector Machine (SVM), the automated method shows a high level of agreement (90.5%) on unseen data with ratings of human experts.

[1]  Andreas Ziehe,et al.  Combining sparsity and rotational invariance in EEG/MEG source reconstruction , 2008, NeuroImage.

[2]  Saeid Sanei,et al.  Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm , 2005, IEEE Signal Processing Letters.

[3]  [Automatic analysis of sleep EEG]. , 2000, Nihon rinsho. Japanese journal of clinical medicine.

[4]  P. Rossini,et al.  Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals , 2004, Clinical Neurophysiology.

[5]  Andreas Ziehe,et al.  A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation , 2004, J. Mach. Learn. Res..

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

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

[8]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[9]  J. Gotman,et al.  A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification , 2006, Clinical Neurophysiology.

[10]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[11]  M.A. Mananas,et al.  Evaluation of an automatic ocular filtering method for awake spontaneous EEG signals based on independent component analysis , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[13]  Klaus-Robert Müller,et al.  Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring , 2008, Journal of Neuroscience Methods.