Eye Blink Artefact Removal of Single Frontal EEG Channel Algorithm using Ensemble Empirical Mode Decomposition and Outlier Detection

Authors

  • Mohd Nurul Al Hafiz Sha'abani Universiti Tun Hussein Onn Malaysia
  • Norfaiza Fuad Universiti Tun Hussein Onn Malaysia
  • Norezmi Jamal Universiti Tun Hussein Onn Malaysia

DOI:

https://doi.org/10.11113/mjfas.v17n6.2287

Keywords:

electroencephalogram, eye blink removal, single-channel, EEMD

Abstract

Recently, the emergence of various applications to use EEG has evolved the EEG device to become wearable with fewer electrodes. Unfortunately, the process of removing artefact becomes challenging since the conventional method requires an additional artefact reference channel or multichannel recording to be working. By focusing on frontal EEG channel recording, this paper proposed an alternative single-channel eye blink artefact removal method based on the ensemble empirical mode decomposition and outlier detection technique. The method removes the segment of the potential eyeblinks artefact on the residual of a pre-determined level of decomposition. An outlier detection technique is introduced to identify the peak of the eyeblink based on the extreme value of the residual signal. The results showed that the corrected EEG signal achieved high correlation, low RMSE and have small differences in PSD when compared to the reference clean EEG. Comparing with an adaptive Wiener filter technique, the corrected EEG signal by the proposed method had better signal-to-artefact ratio.

References

R. W. Homan, J. Herman, and P. Purdy, "Cerebral location of international 10–20 system electrode placement," Electroencephalography Clinical Neurophysiology, vol. 66, no. 4, pp. 376-382, 1987.

M. Rashid, N. Sulaiman, A. P. P. Abdul Majeed et al., "Current status, challenges, and possible solutions of eeg-based brain-computer interface: A comprehensive review," Frontiers in Neurorobotics, Review vol. 14, no. 25, 2020.

R. Abreu, M. Leite, A. Leal, and P. Figueiredo, "Objective selection of epilepsy-related independent components from eeg data," Journal of Neuroscience Methods, vol. 258, pp. 67-78, 2016.

M. S. Fathillah, R. Jaafar, K. Chellappan et al., "Multiresolution analysis on nonlinear complexity measurement of eeg signal for epileptic discharge monitoring," Malaysian Journal of Fundamental Applied Sciences, vol. 14, no. 2, pp. 219-225, 2018.

H. Lim and J. Ku, "Multiple-command single-frequency ssvep-based bci system using flickering action video," Journal of Neuroscience Methods, vol. 314, pp. 21-27, 2019.

M. F. M. Rafi, A. R. A. Harris, T. T. Swee et al., "Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment," Malaysian Journal of Fundamental Applied Sciences, vol. 16, no. 2, pp. 236-242, 2020.

N. Fuad, J. Bakar, M. N. Danial, E. Nasir, and M. Marwan, "A comparative study of learning methodology between cognitive and psychomotor for non-dyslexia person via electroencephalogram (eeg)," International Journal of Scientific and Technology Research, vol. 8, no. 7, 2019.

M. Gasah, A. Baharum, and N. H. M. Zain, "Measure learning effectiveness among children using eeg device and mobile application," Indonesian Journal of Electrical Engineering and Computer Science, Review vol. 17, no. 1, pp. 191-196, 2019.

A. J. Casson, "Wearable eeg and beyond," Biomedical Engineering Letters, vol. 9, no. 1, pp. 53-71, 2019.

M. Teplan, "Fundamentals of eeg measurement," Measurement Science Review, vol. 2, no. 2, pp. 1-11, 2002.

S. Halder, M. Bensch, J. Mellinger et al., "Online artifact removal for brain-computer interfaces using support vector machines and blind source separation," Computational Intelligence Neuroscience, vol. 2007, 2007.

S. Sreeja, R. R. Sahay, D. Samanta, and P. Mitra, "Removal of eye blink artifacts from eeg signals using sparsity," IEEE Journal of Biomedical Health Informatics, vol. 22, no. 5, pp. 1362-1372, 2017.

M. K. Islam, A. Rastegarnia, and Z. Yang, "Methods for artifact detection and removal from scalp eeg: A review," Neurophysiologie Clinique/Clinical Neurophysiology, vol. 46, no. 4, pp. 287-305, 2016.

S. Zhang, J. McIntosh, S. M. Shadli et al., "Removing eye blink artefacts from eeg—a single-channel physiology-based method," Journal of Neuroscience Methods, vol. 291, pp. 213-220, 2017.

P. Comon, "Independent component analysis, a new concept?," Signal Processing, vol. 36, no. 3, pp. 287-314, 1994.

X. Jiang, G.-B. Bian, and Z. Tian, "Removal of artifacts from eeg signals: A review," Sensors, vol. 19, no. 5, p. 987, 2019.

W. K. So, S. W. Wong, J. N. Mak, and R. H. Chan, "An evaluation of mental workload with frontal eeg," PloS one, vol. 12, no. 4, p. e0174949, 2017.

J. A. Urigüen and B. Garcia-Zapirain, "Eeg artifact removal—state-of-the-art and guidelines," Journal of Neural Engineering, vol. 12, no. 3, p. 031001, 2015.

K. T. Sweeney, T. E. Ward, and S. F. McLoone, "Artifact removal in physiological signals—practices and possibilities," IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 488-500, 2012.

X. Chen, X. Xu, A. Liu, M. J. McKeown, and Z. J. Wang, "The use of multivariate emd and cca for denoising muscle artifacts from few-channel eeg recordings," IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 2, pp. 359-370, 2017.

N. E. Huang, Z. Shen, S. R. Long et al., "The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," Journal Proceedings of the Royal Society of London. Series A: Mathematical, Physical Engineering Sciences, vol. 454, no. 1971, pp. 903-995, 1998.

B. Mijović, M. De Vos, I. Gligorijević, J. Taelman, and S. Van Huffel, "Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis," IEEE Transactions on Biomedical Engineering, vol. 57, no. 9, pp. 2188-2196, 2010.

K. Zeng, D. Chen, G. Ouyang et al., "An eemd-ica approach to enhancing artifact rejection for noisy multivariate neural data," IEEE Transactions on Neural Systems Rehabilitation Engineering, vol. 24, no. 6, pp. 630-638, 2015.

Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: A noise-assisted data analysis method," Journal Advances in Adaptive Data Analysis, vol. 1, no. 01, pp. 1-41, 2009.

M. N. A. H. Sha’abani, N. Fuad, N. Jamal et al., "Development of cognitive and psychomotor task for eeg application with matlab-based gui," in IOP Conference Series: Materials Science and Engineering, 2020, vol. 917, no. 1, p. 012050: IOP Publishing.

K. Kleifges, N. Bigdely-Shamlo, S. E. Kerick, and K. A. Robbins, "Blinker: Automated extraction of ocular indices from eeg enabling large-scale analysis," Frontiers in Neuroscience, vol. 11, p. 12, 2017.

I. Zyma, S. Tukaev, I. Seleznov et al., "Electroencephalograms during mental arithmetic task performance," Data, vol. 4, no. 1, p. 14, 2019.

J. R. Wolpaw and E. W. Wolpaw, Brain-computer interfaces: Principles and practice. OUP USA, 2012.

R. Patel, M. P. Janawadkar, S. Sengottuvel, K. Gireesan, and T. S. Radhakrishnan, "Suppression of eye-blink associated artifact using single channel eeg data by combining cross-correlation with empirical mode decomposition," IEEE Sensors Journal, vol. 16, no. 18, pp. 6947-6954, 2016.

M. H. Soomro, N. Badruddin, and M. Z. Yusoff, "Comparison of blind source separation methods for removal of eye blink artifacts from eeg," in 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), 2014, pp. 1-6: IEEE.

J. Ferdous, S. Ali, E. Hamid, and K. I. Molla, "Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform," International Journal of Advanced Robotic Systems, vol. 18, no. 1, 2021.

Downloads

Published

31-12-2021