Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG

BACKGROUND Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. NEW METHOD In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. RESULTS Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)-an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. COMPARISON WITH EXISTING METHOD(S) Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. CONCLUSIONS Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.

[1]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[2]  Banghua Yang,et al.  Combination of wavelet packet transform and Hilbert-Huang transform for recognition of continuous EEG in BCIs , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[3]  Christopher J. James,et al.  On Semi-Blind Source Separation Using Spatial Constraints With Applications in EEG Analysis , 2006, IEEE Transactions on Biomedical Engineering.

[4]  Tania S. Douglas,et al.  Motion Artifact Removal for Functional Near Infrared Spectroscopy: A Comparison of Methods , 2010, IEEE Transactions on Biomedical Engineering.

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

[6]  Duoqian Miao,et al.  Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection , 2011, Expert Syst. Appl..

[7]  M. De Vos,et al.  Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging , 2009, Clinical Neurophysiology.

[8]  Sabine Van Huffel,et al.  Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis , 2010, IEEE Transactions on Biomedical Engineering.

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

[10]  Wenjing Su,et al.  Automatic Removal of Artifacts from Attention Deficit Hyperactivity Disorder Electroencephalograms Based on Independent Component Analysis , 2012, Cognitive Computation.

[11]  Bin Hu,et al.  A pervasive EEG-based biometric system , 2011, UAAII '11.

[12]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

[13]  Bijaya K. Panigrahi,et al.  A comparative study of wavelet families for EEG signal classification , 2011, Neurocomputing.

[14]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[15]  Christopher J. James,et al.  Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data , 2012, Signal Process..

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

[17]  F Babiloni,et al.  Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[18]  Koushik Maharatna,et al.  Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Christoph M. Michel,et al.  Towards the utilization of EEG as a brain imaging tool , 2012, NeuroImage.

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

[21]  Hasan Ayaz,et al.  A Methodology for Validating Artifact Removal Techniques for Physiological Signals , 2012, IEEE Transactions on Information Technology in Biomedicine.

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

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

[24]  C. W. Hesse,et al.  The FastICA algorithm with spatial constraints , 2005, IEEE Signal Processing Letters.

[25]  Marc Moonen,et al.  Joint DOA and multi-pitch estimation based on subspace techniques , 2012, EURASIP J. Adv. Signal Process..

[26]  Desire L. Massart,et al.  Noise suppression and signal compression using the wavelet packet transform , 1997 .

[27]  Seán F. McLoone,et al.  The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique , 2013, IEEE Transactions on Biomedical Engineering.

[28]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[29]  M Zima,et al.  Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions. , 2012, Physiological measurement.

[30]  Begoña Garcia-Zapirain,et al.  EEG artifact removal—state-of-the-art and guidelines , 2015, Journal of neural engineering.

[31]  K H Ting,et al.  Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only. , 2006, Medical engineering & physics.

[32]  Rabab Kreidieh Ward,et al.  A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG , 2014, Sensors.

[33]  S. M. Gordon,et al.  Informed decomposition of electroencephalographic data , 2015, Journal of Neuroscience Methods.

[34]  Clay B. Holroyd,et al.  Detection of synchronized oscillations in the electroencephalogram: an evaluation of methods. , 2004, Psychophysiology.

[35]  Wim Van Paesschen,et al.  Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[36]  D. Narayana Dutt,et al.  Application of LMS adaptive predictive filtering for muscle artifact (noise) cancellation from EEG signals , 1996 .

[37]  Yanda Li,et al.  Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach , 2006, Physiological measurement.

[38]  Saeid Sanei,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[39]  Lotfi Senhadji,et al.  Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches , 2012, EURASIP J. Adv. Signal Process..

[40]  Francesco Carlo Morabito,et al.  Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy , 2008, Neural Networks.

[41]  W. van Paesschen,et al.  Improving the Interpretation of Ictal Scalp EEG: BSS–CCA Algorithm for Muscle Artifact Removal , 2007, Epilepsia.

[42]  Tomás Ward,et al.  Artifact Removal in Physiological Signals—Practices and Possibilities , 2012, IEEE Transactions on Information Technology in Biomedicine.

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

[44]  Allen D. Malony,et al.  A Framework for Evaluating ICA Methods of Artifact Removal from Multichannel EEG , 2004, ICA.

[45]  V. A. Makarov,et al.  Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis , 2006, Journal of Neuroscience Methods.

[46]  Terrence J. Sejnowski,et al.  AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS , 2001 .

[47]  Andrzej Cichocki,et al.  EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts , 2008, Physiological measurement.

[48]  Luay Fraiwan,et al.  Automatic Sleep Stage Scoring with Wavelet Packets Based on Single EEG Recording , 2009 .

[49]  Nadine Eberhardt,et al.  Bioelectrical Signal Processing In Cardiac And Neurological Applications , 2016 .

[50]  Mercedes Atienza,et al.  Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis , 2008, Annals of Biomedical Engineering.