Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG

For biomedical and scientific fields, Electroencephalography (EEG) has turned out to be an important tool to understand, study, and utilize brain functionalities. To fully utilize EEG signals in real-life closed-loop applications, artifacts such as ocular must be removed. Wavelet transform is one of the powerful methods to remove ocular artifacts from single channel EEG devices. In this study, both stationary and discrete wavelet transforms (SWT and DWT, respectively) have been compared with various wavelet basis functions, such as sym3, haar, coif3, and bior4.4 using either universal threshold (UT) or statistical threshold (ST). Different combinations of wavelet transform techniques, mother wavelets, and thresholds are compared to identify an optimum combination for ocular artifact removal. Performance metrics like Correlation Coefficient (CC), Normalized Mean Square Error (NMSE), Time Frequency Analysis, and execution time have been calculated for measuring the effectiveness of each combination. According to CC, DWT+UT combination turned out to be a good option for the ocular artifact removal. However, according to NMSE and time frequency analysis, SWT+ST has generated better performance in keeping neural segments of EEG unaffected. According to the measurement of execution times, DWT+ST is faster compared to other combinations. The study shows that wavelet transform is suitable in artifact removal from single channel EEG data to implement in ambulatory real-time EEG systems.

[1]  Bashir I. Morshed,et al.  Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA , 2015, IEEE Journal of Biomedical and Health Informatics.

[2]  Jürgen Kurths,et al.  Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography , 1998 .

[3]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[4]  T. Lagerlund,et al.  Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  V. Samar,et al.  Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial , 1999, Brain and Language.

[6]  Glenn F. Wilson,et al.  Removal of ocular artifacts from the EEG: a comparison between time-domain regression method and adaptive filtering method using simulated data , 2007, Medical & Biological Engineering & Computing.

[7]  M. Huzmezan,et al.  A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[8]  Md Nazmus Sahadat,et al.  Wireless ambulatory ECG signal capture for HRV and cognitive load study using the NeuroMonitor platform , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

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

[10]  Guoping Gao,et al.  Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications , 2013, IEEE Journal of Biomedical and Health Informatics.

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

[12]  J. C. Woestenburg,et al.  The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain , 1983, Biological Psychology.

[13]  L. Vigon,et al.  Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms , 2000 .

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

[15]  V. Krishnaveni,et al.  Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients , 2006, Journal of neural engineering.

[16]  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..