Enhanced ${\mu }$ Rhythm Extraction Using Blind Source Separation and Wavelet Transform

The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.

[1]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.

[2]  N. Erbil,et al.  Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements , 2007, Brain Research.

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

[4]  G. Cheron,et al.  Effect of gravity on human spontaneous 10-Hz electroencephalographic oscillations during the arrest reaction , 2006, Brain Research.

[5]  F.C. Morabito,et al.  Neural-ICA and wavelet transform for artifacts removal in surface EMG , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[6]  Weidong Zhou,et al.  Removal of ECG artifacts from EEG using ICA , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[7]  J. Polich,et al.  P300 and blink instructions , 2000, Clinical Neurophysiology.

[8]  Francesco Carlo Morabito,et al.  Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings , 2007, 2007 International Joint Conference on Neural Networks.

[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]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[11]  Richard G. Shiavi,et al.  Wavelet Methods for Spike Detection in Mouse Renal Sympathetic Nerve Activity , 2007, IEEE Transactions on Biomedical Engineering.

[12]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[13]  Ritske de Jong,et al.  Movement-related EEG indices of preparation in task switching and motor control , 2006, Brain Research.

[14]  P. Derambure,et al.  Influence of aging on cortical activity associated with a visuo-motor task , 2004, Neurobiology of Aging.

[15]  Naznin Virji-Babul,et al.  Changes in mu rhythm during action observation and execution in adults with Down syndrome: Implications for action representation , 2008, Neuroscience Letters.

[16]  G. Pfurtscheller,et al.  Foot and hand area mu rhythms. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[17]  G. Pfurtscheller,et al.  The effects of external load on movement-related changes of the sensorimotor EEG rhythms. , 1997, Electroencephalography and clinical neurophysiology.

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

[19]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[20]  W. De Clercq,et al.  Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[21]  Richard Shiavi,et al.  Spike detection in human muscle sympathetic nerve activity using the kurtosis of stationary wavelet transform coefficients , 2007, Journal of Neuroscience Methods.

[22]  Tatiana A. Stroganova,et al.  Abnormal EEG lateralization in boys with autism , 2007, Clinical Neurophysiology.

[23]  J. Pineda,et al.  Positive behavioral and electrophysiological changes following neurofeedback training in children with autism , 2008 .

[24]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[25]  L. Euginia,et al.  A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram , 2004, Proceedings. The 8th Russian-Korean International Symposium on Science and Technology, 2004. KORUS 2004..

[26]  A. Cichocki,et al.  Robust whitening procedure in blind source separation context , 2000 .

[27]  Scott Makeig,et al.  Information-based modeling of event-related brain dynamics. , 2006, Progress in brain research.

[28]  Cornelis J. Stam,et al.  A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements , 2005, Artif. Intell. Medicine.

[29]  R. Srinivasan,et al.  Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique , 1999, IEEE Signal Processing Letters.

[30]  J Diez,et al.  Event-related desynchronization and synchronization in idiopathic Parkinson's disease , 1997 .

[31]  Lotfi Senhadji,et al.  Extraction and separation of eyes movements and the muscular tonus from a restricted number of electrodes using the independent component analysis , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

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

[33]  Miguel Angel Mañanas,et al.  A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case , 2008, Comput. Biol. Medicine.

[34]  Chwan-Lu Tseng,et al.  An automatic analysis method for detecting and eliminating ECG artifacts in EEG , 2007, Comput. Biol. Medicine.

[35]  D E Blum,et al.  Computer-based electroencephalography: technical basics, basis for new applications, and potential pitfalls. , 1998, Electroencephalography and clinical neurophysiology.

[36]  J.R. Wolpaw,et al.  A $\mu $-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface , 2007, IEEE Transactions on Biomedical Engineering.

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

[38]  R. Kass,et al.  Bayesian curve-fitting with free-knot splines , 2001 .

[39]  G. Birch,et al.  Time-frequency Analysis of Eye Blinks and Saccades in EOG for EEG Artifact Removal , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[40]  Christian Vasseur,et al.  Filtering by optimal projection and application to automatic artifact removal from EEG , 2007, Signal Process..

[41]  F.C. Morabito,et al.  Brain Activity Investigation by EEG Processing: Wavelet Analysis, Kurtosis and Renyi's Entropy for Artifact Detection , 2007, 2007 International Conference on Information Acquisition.

[42]  J. Gotman,et al.  Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[44]  E. Oja,et al.  Independent Component Analysis , 2013 .

[45]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[46]  Joep J. M. Kierkels,et al.  A model-based objective evaluation of eye movement correction in EEG recordings , 2006, IEEE Transactions on Biomedical Engineering.

[47]  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.