Real-time Single-Channel EOG removal based on Empirical Mode Decomposition

Kien Nguyen Trong; Nhat Nguyen Luong; Hanh Tan; Duy Tran Trung; Huong Ha Thi Thanh; Duy Pham The; Binh Nguyen Thanh

doi:10.4108/eetinis.v11i2.4593

Authors

Kien Nguyen Trong Posts and Telecommunications Institute of Technology
Nhat Nguyen Luong Posts and Telecommunications Institute of Technology
Hanh Tan Posts and Telecommunications Institute of Technology
Duy Tran Trung Posts and Telecommunications Institute of Technology
Huong Ha Thi Thanh Ho Chi Minh City International University
Duy Pham The Posts and Telecommunications Institute of Technology
Binh Nguyen Thanh Posts and Telecommunications Institute of Technology

DOI:

https://doi.org/10.4108/eetinis.v11i2.4593

Keywords:

Empirical mode composition, Electrooculogram artifacts, single-channel artefact removal

Abstract

In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.

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References

M. Miao, W. Hu, B. Xu, J. Zhang, J. J. P. C. Rodrigues, and V. H. C. de Albuquerque, “Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG,” IEEE J Biomed Health Inform, vol. 26, no. 8, pp. 3607–3617, 2022, doi: 10.1109/JBHI.2021.3131186. DOI: https://doi.org/10.1109/JBHI.2021.3131186

L. Meng et al., “Evaluation of decomposition parameters for high-density surface electromyogram using fast independent component analysis algorithm,” Biomed Signal Process Control, vol. 75, p. 103615, 2022, doi: https://doi.org/10.1016/j.bspc.2022.103615. DOI: https://doi.org/10.1016/j.bspc.2022.103615

N. Mourad, “ECG denoising based on successive local filtering,” Biomed Signal Process Control, vol. 73, p. 103431, 2022, doi: https://doi.org/10.1016/j.bspc.2021.103431. DOI: https://doi.org/10.1016/j.bspc.2021.103431

M. Klug and K. Gramann, “Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments,” European Journal of Neuroscience, vol. 54, no. 12, pp. 8406–8420, Dec. 2021, doi: https://doi.org/10.1111/ejn.14992. DOI: https://doi.org/10.1111/ejn.14992

G. Rebolledo-Mendez et al., “Assessing NeuroSky’s Usability to Detect Attention Levels in an Assessment Exercise,” in Human-Computer Interaction. New Trends, J. A. Jacko, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 149–158. DOI: https://doi.org/10.1007/978-3-642-02574-7_17

S.-F. Liang, C.-E. Kuo, Y.-H. Hu, Y.-H. Pan, and Y.-H. Wang, “Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models,” IEEE Trans Instrum Meas, vol. 61, no. 6, pp. 1649–1657, 2012, doi: 10.1109/TIM.2012.2187242. DOI: https://doi.org/10.1109/TIM.2012.2187242

B. R. Greene, G. B. Boylan, W. P. Marnane, G. Lightbody, and S. Connolly, “Automated single channel seizure detection in the neonate,” in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 915–918. doi: 10.1109/IEMBS.2008.4649303. DOI: https://doi.org/10.1109/IEMBS.2008.4649303

S. Ge, R. Wang, and D. Yu, “Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography,” PLoS One, vol. 9, no. 6, pp. e98019-, Jun. 2014, [Online]. Available: https://doi.org/10.1371/journal.pone.0098019 DOI: https://doi.org/10.1371/journal.pone.0098019

C. Wan, D. Chen, Z. Huang, and X. Luo, “A wearable head mounted display bio-signals pad system for emotion recognition,” Sensors, vol. 22, no. 1, Jan. 2022, doi: 10.3390/s22010142. DOI: https://doi.org/10.3390/s22010142

S. Kwon, H. Kim, and W.-H. Yeo, “Recent advances in wearable sensors and portable electronics for sleep monitoring,” iScience, vol. 24, no. 5, p. 102461, 2021, doi: https://doi.org/10.1016/j.isci.2021.102461. DOI: https://doi.org/10.1016/j.isci.2021.102461

G. Inuso, F. La Foresta, N. Mammone, and F. C. Morabito, “Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings,” in 2007 International Joint Conference on Neural Networks, 2007, pp. 1524–1529. doi: 10.1109/IJCNN.2007.4371184. DOI: https://doi.org/10.1109/IJCNN.2007.4371184

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 Trans Biomed Eng, vol. 57, no. 9, pp. 2188–2196, 2010, doi: 10.1109/TBME.2010.2051440. DOI: https://doi.org/10.1109/TBME.2010.2051440

K. T. Sweeney, S. Member, S. F. McLoone, S. Member, and T. E. Ward, “The use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique.”

J. Yedukondalu and L. D. Sharma, “Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals,” Sensors, vol. 23, no. 3, Feb. 2023, doi: 10.3390/s23031235. DOI: https://doi.org/10.3390/s23031235

C. Liu and C. Zhang, “Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI,” Sensors, vol. 22, no. 17, Sep. 2022, doi: 10.3390/s22176698.

M. Wang, X. Cui, T. Wang, T. Jiang, F. Gao, and J. Cao, “Eye blink artifact detection based on multi-dimensional EEG feature fusion and optimization,” Biomed Signal Process Control, vol. 83, p. 104657, 2023, doi: https://doi.org/10.1016/j.bspc.2023.104657. DOI: https://doi.org/10.1016/j.bspc.2023.104657

Y. Li, A. Liu, J. Yin, C. Li, and X. Chen, “A Segmentation-Denoising Network for Artifact Removal From Single-Channel EEG,” IEEE Sens J, vol. 23, no. 13, pp. 15115–15127, 2023, doi: 10.1109/JSEN.2023.3276481. DOI: https://doi.org/10.1109/JSEN.2023.3276481

J. A. Urigüen and B. Garcia-Zapirain, “EEG artifact removal—state-of-the-art and guidelines,” J Neural Eng, vol. 12, no. 3, p. 031001, 2015, doi: 10.1088/1741-2560/12/3/031001. DOI: https://doi.org/10.1088/1741-2560/12/3/031001

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, doi: 10.1109/TITB.2012.2188536. DOI: https://doi.org/10.1109/TITB.2012.2188536

L. Vigon, M. Saatchi, J. E. W. Mayhew, and R. Fernandes, “Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms,” 2000. [Online]. Available: https://api.semanticscholar.org/CorpusID:61818742 DOI: https://doi.org/10.1049/ip-smt:20000475

S. Makeig, A. Bell, T.-P. Jung, and T. J. Sejnowski, “Independent Component Analysis of Electroencephalographic Data,” in Advances in Neural Information Processing Systems, D. Touretzky, M. C. Mozer, and M. Hasselmo, Eds., MIT Press, 1995. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/1995/file/754dda4b1ba34c6fa89716b85d68532b-Paper.pdf

P. Yuan, L. Jiang, J. Liu, D. Zhou, P. Li, and Y. Gao, “Dual-Level Attention Based on Heterogeneous Graph Convolution Network for Aspect-Based Sentiment Classification,” in 2020 IEEE International Conference on Smart Cloud (SmartCloud), 2020, pp. 74–77. doi: 10.1109/SmartCloud49737.2020.00022. DOI: https://doi.org/10.1109/SmartCloud49737.2020.00022

L. Jiang, J. Liu, D. Zhou, Q. Zhou, X. Yang, and G. Yu, “Predicting the Evolution of Hot Topics: A Solution Based on the Online Opinion Dynamics Model in Social Network,” IEEE Trans Syst Man Cybern Syst, vol. 50, no. 10, pp. 3828–3840, 2020, doi: 10.1109/TSMC.2018.2876235. DOI: https://doi.org/10.1109/TSMC.2018.2876235

P. Senthil Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal, “Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel,” 2008.

N. Mammone, F. La Foresta, and F. C. Morabito, “Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA,” IEEE Sens J, vol. 12, no. 3, pp. 533–542, 2012, doi: 10.1109/JSEN.2011.2115236. DOI: https://doi.org/10.1109/JSEN.2011.2115236

N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, Mar. 1998, doi: 10.1098/rspa.1998.0193.

Z. Wu and N. E. Huang, “Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method,” Adv Adapt Data Anal, vol. 01, no. 01, pp. 1–41, 2009. DOI: https://doi.org/10.1142/S1793536909000047

M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 4144–4147. doi: 10.1109/ICASSP.2011.5947265. DOI: https://doi.org/10.1109/ICASSP.2011.5947265

M. A. Colominas, G. Schlotthauer, and M. E. Torres, “Improved complete ensemble EMD: A suitable tool for biomedical signal processing,” Biomed Signal Process Control, vol. 14, no. 1, pp. 19–29, 2014, doi: 10.1016/j.bspc.2014.06.009. DOI: https://doi.org/10.1016/j.bspc.2014.06.009

A. Mayeli, V. Zotev, H. Refai, and J. Bodurka, “Real-time EEG artifact correction during fMRI using ICA,” J Neurosci Methods, vol. 274, pp. 27–37, 2016, doi: https://doi.org/10.1016/j.jneumeth.2016.09.012. DOI: https://doi.org/10.1016/j.jneumeth.2016.09.012

F. Esposito et al., “Real-time independent component analysis of fMRI time-series,” Neuroimage, vol. 20, no. 4, pp. 2209–2224, 2003, doi: https://doi.org/10.1016/j.neuroimage.2003.08.012. DOI: https://doi.org/10.1016/j.neuroimage.2003.08.012

J. Onton, M. Westerfield, J. Townsend, and S. Makeig, “Imaging human EEG dynamics using independent component analysis,” Neurosci Biobehav Rev, vol. 30, no. 6, pp. 808–822, 2006, doi: https://doi.org/10.1016/j.neubiorev.2006.06.007. DOI: https://doi.org/10.1016/j.neubiorev.2006.06.007

C. Liu and C. Zhang, “Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI,” Sensors, vol. 22, no. 17, Sep. 2022, doi: 10.3390/s22176698. DOI: https://doi.org/10.3390/s22176698

P. Flandrin, G. Rilling, and P. Gonçalvés, “Empirical mode decomposition as a filter bank,” IEEE Signal Process Lett, vol. 11, no. 2 PART I, pp. 112–114, 2004, doi: 10.1109/LSP.2003.821662. DOI: https://doi.org/10.1109/LSP.2003.821662

N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. DOI: https://doi.org/10.1098/rspa.1998.0193

R. Deering and J. F. Kaiser, “The use of a masking signal to improve empirical mode decomposition,” in Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005, p. iv/485-iv/488 Vol. 4. doi: 10.1109/ICASSP.2005.1416051. DOI: https://doi.org/10.1109/ICASSP.2005.1416051

F. F. Tsai, S. Z. Fan, Y. S. Lin, N. E. Huang, and J. R. Yeh, “Investigating power density and the degree of nonlinearity in intrinsic components of anesthesia EEG by the hilbert-huang transform: An example using ketamine and alfentanil,” PLoS One, vol. 11, no. 12, pp. 1–17, 2016, doi: 10.1371/journal.pone.0168108. DOI: https://doi.org/10.1371/journal.pone.0168108

K. T. Nguyen et al., “Unraveling nonlinear electrophysiologic processes in the human visual system with full dimension spectral analysis,” Sci Rep, vol. 9, no. 1, p. 16919, 2019, doi: 10.1038/s41598-019-53286-z. DOI: https://doi.org/10.1038/s41598-019-53286-z

C.-H. Juan et al., “Revealing the Dynamic Nature of Amplitude Modulated Neural Entrainment With Holo-Hilbert Spectral Analysis,” Front Neurosci, vol. 15, p. 977, 2021, doi: 10.3389/fnins.2021.673369. DOI: https://doi.org/10.3389/fnins.2021.673369

M. A. Klados and P. D. Bamidis, “A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques,” 2016, doi: 10.1109/JSEN. DOI: https://doi.org/10.1016/j.dib.2016.06.032

M. Dora and D. Holcman, “Adaptive Single-Channel EEG Artifact Removal with Applications to Clinical Monitoring,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 286–295, 2022, doi: 10.1109/TNSRE.2022.3147072. DOI: https://doi.org/10.1109/TNSRE.2022.3147072