Classification of independent components of EEG into multiple artifact classes.

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them.

[1]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[2]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[3]  John Langford,et al.  Cost-sensitive learning by cost-proportionate example weighting , 2003, Third IEEE International Conference on Data Mining.

[4]  Soo-Young Lee,et al.  FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling , 2003, IEEE Trans. Neural Networks.

[5]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[6]  Ilkka Korhonen,et al.  The effect of interruption to propofol sedation on auditory event-related potentials and electroencephalogram in intensive care patients , 2004, Critical care.

[7]  A. Erfanian,et al.  ICA-based classification scheme for EEG-based brain-computer interface: the role of mental practice and concentration skills , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Giovanni Cancellieri,et al.  Independent component analysis: fetal signal reconstruction from magnetocardiographic recordings , 2004, Comput. Methods Programs Biomed..

[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]  G. Wilson,et al.  Removal of ocular artifacts from electro-encephalogram by adaptive filtering , 2004, Medical and Biological Engineering and Computing.

[11]  Di Lai,et al.  Independent Component Analysis Applied to Ultrasound Speckle Texture Analysis and Tissue Characterization , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  R. Ward,et al.  EMG and EOG artifacts in brain computer interface systems: A survey , 2007, Clinical Neurophysiology.

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

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

[15]  Scott Makeig,et al.  Head modeling and cortical source localization in epilepsy , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Gian Luca Romani,et al.  Improving MEG source localizations: An automated method for complete artifact removal based on independent component analysis , 2008, NeuroImage.

[17]  H. Gunshin,et al.  A review of independent component analysis application to microarray gene expression data. , 2008, BioTechniques.

[18]  Du-Ming Tsai,et al.  Independent Component Analysis-Based Background Subtraction for Indoor Surveillance , 2009, IEEE Transactions on Image Processing.

[19]  Scott Makeig,et al.  High-frequency Broadband Modulations of Electroencephalographic Spectra , 2009, Front. Hum. Neurosci..

[20]  Paul C. Fletcher,et al.  Why Do Delusions Persist? , 2009, Frontiers in Human Neuroscience.

[21]  Piotr J. Durka,et al.  On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings , 2009, Neuroinformatics.

[22]  Michael Tangermann,et al.  Classification of Artifactual ICA Components , 2009 .

[23]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[24]  Tom Eichele,et al.  Semi-automatic identification of independent components representing EEG artifact , 2009, Clinical Neurophysiology.

[25]  P. Federico.  Simultaneous Eeg and Fmri: Recording, Analysis and Application , 2010, Neurology.

[26]  Klaus Gramann,et al.  Dimension-based attention modulates early visual processing. , 2010, Psychophysiology.

[27]  Luca Citi,et al.  Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller , 2010, Journal of neural engineering.

[28]  Bao-Liang Lu,et al.  Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[29]  R. B. Reilly,et al.  FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection , 2010, Journal of Neuroscience Methods.

[30]  Till R. Schneider,et al.  Using ICA for the Analysis of Multi-Channel EEG Data , 2010 .

[31]  M. Tangermann,et al.  Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals , 2011, Behavioral and Brain Functions.

[32]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[33]  A. Santhakumaran,et al.  Statistical Normalization and Back Propagationfor Classification , 2011 .

[34]  Motoaki Kawanabe,et al.  Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation , 2012, Adaptive computation and machine learning.

[35]  Francisco Herrera,et al.  Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics , 2012, Expert Syst. Appl..

[36]  Kenneth Kreutz-Delgado,et al.  EyeCatch: Data-mining over half a million EEG independent components to construct a fully-automated eye-component detector , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[37]  S. Mohamed,et al.  Statistical Normalization and Back Propagation for Classification , 2022 .