Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis_Journal of Biomedical Engineering

Authors：

SONG Hao ^1,2 , XU Song ^3,4 , LIU Guoming ³ , LIU Jing ⁵ ,  XIONG Peng ^1,6

1. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P. R. China;
2. School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P. R. China;
3. Beijing Aerospace Automatic Control Institute, Beijing 100854, P. R. China;
4. National Key Laboratory of Aerospace Intelligent Control Technology, Beijing 100854, P. R. China;
5. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, P. R. China;
6. College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P. R. China;

Corresponding author：

XIONG Peng, Email: xiongde.youxiang@163.com

Keywords：

Electroencephalography; Independent component analysis; Filter; Ocular artifact removal; Non-invasive brain computer interface

DOI：

10.7507/1001-5515.202111060

Video：

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The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.

Citation： SONG Hao, XU Song, LIU Guoming, LIU Jing, XIONG Peng. Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis. Journal of Biomedical Engineering, 2022, 39(6): 1074-1081. doi: 10.7507/1001-5515.202111060 Copy

1.	Wang G, Wang D, Du C, et al. Seizure prediction using directed transfer function and convolution neural network on intracranial EEG. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(12): 2711-2720.
2.	Li Y, Shi W, Liu Z, et al. Effective brain state estimation during propofol-induced sedation using advanced EEG microstate spectral analysis. IEEE J Biomed Health Inform, 2020, 25(4): 978-987.
3.	Sivathamboo S, Farrand S, Chen Z, et al. Sleep-disordered breathing among patients admitted for inpatient video-EEG monitoring. Neurology, 2019, 92(3): e194-e204.
4.	Gong R, Wegscheider M, Mühlberg C, et al. Spatiotemporal features of β-γ phase-amplitude coupling in Parkinson’s disease derived from scalp EEG. Brain, 2021, 144(2): 487-503.
5.	Wen D, Fan Y, Hsu S H, et al. Combining brain–computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review. Ann Phys Rehabil Med, 2021, 64(1): 101404.
6.	Urigüen J A, Garcia Z B. EEG artifact removal—state-of-the-art and guidelines. J Neural Eng, 2015, 12(3): 031001.
7.	Egambaram A, Badruddin N, Asirvadam V S, et al. FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram. Biomed Signal Process Control, 2020, 57: 101692.
8.	Sun W, Su Y, Wu X, et al. A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals. Neurocomputing, 2020, 404: 108-121.
9.	Fatourechi M, Bashashati A, Ward R K, et al. EMG and EOG artifacts in brain computer interface systems: A survey. Clin Neurophysiol, 2007, 118(3): 480-494.
10.	Klados M A, Papadelis C, Braun C, et al. REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts. Biomed Signal Process Control, 2011, 6(3): 291-300.
11.	Islam M K, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiol Clin, 2016, 46(4-5): 287-305.
12.	Chen Y, Zhao Q, Hu B, et al. A method of removing ocular artifacts from EEG using discrete wavelet transform and Kalman filtering// 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Shenzhen: IEEE, 2016: 1485-1492.
13.	Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal, 2009, 1(01): 1-41.
14.	杨磊, 杨帆, 何艳. 采用样本熵-完备经验模态分解的脑电信号眼电伪迹去除算法. 西安交通大学学报, 2020, 54(8): 177-184.
15.	Guarnieri R, Marino M, Barban F, et al. Online EEG artifact removal for BCI applications by adaptive spatial filtering. J Neural Eng, 2018, 15(5): 056009.
16.	Burger C, van den Heever D J. Removal of EOG artefacts by combining wavelet neural network and independent component analysis. Biomed Signal Process Control, 2015, 15: 67-79.
17.	Wang Z Y, Xu P, Liu T J, et al. Robust removal of ocular artifacts by combining independent component analysis and system identification. Biomed Signal Process Control, 2014, 10(3): 250-259.
18.	Castellanos N P, Makarov V A. Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods, 2006, 158(2): 300-312.
19.	Pion-Tonachini L, Hsu S H, Chang C Y, et al. Online automatic artifact rejection using the real-time EEG source-mapping toolbox (REST)// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 106-109.
20.	Issa M F, Juhasz Z. Improved EOG artifact removal using wavelet enhanced independent component analysis. Brain Sci, 2019, 9(12): 355.
21.	Durka P J, Klekowicz H, Blinowska K J, et al. A simple system for detection of EEG artifacts in polysomnographic recordings. IEEE Trans Biomed Eng, 2003, 50(4): 526-528.
22.	Trigui O, Daoud S, Ghorbel M, et al. Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection. Signal Image Video Process, 2022, 16: 19-27.
23.	Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage, 2007, 34(4): 1443-1449.
24.	Mammone N, La Foresta F, Morabito F C. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sens J, 2011, 12(3): 533-542.
25.	Teng C L, Zhang Y Y, Wang W, et al. A novel method based on combination of independent component analysis and ensemble empirical mode decomposition for removing electrooculogram artifacts from multichannel electroencephalogram signals. Front Neurosci, 2021, 15: 729403.
26.	Klados M A, Bamidis P D. A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief, 2016, 8: 1004-1006.
27.	张锐, 刘家俊, 陈明明, 等. 基于小波变换-集合经验模态分解的单通道脑电信号眼电伪迹自动去除研究. 生物医学工程学杂志, 2021, 38(3): 473-482.
28.	Majmudar C A, Mahajan R, Morshed B I. Real-time hybrid ocular artifact detection and removal for single channel EEG// 2015 IEEE International Conference on Electro/Information Technology (EIT). Dekalb: IEEE, 2015: 330-334.
29.	Mahajan R, Morshed B I. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE J Biomed Health Inform, 2014, 19(1): 158-165.
30.	Khatun S, Mahajan R, Morshed B I. Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data. IEEE J Transl Eng Health Med, 2016, 4: 1-8.
31.	Malekpour S, Gubner J A, Sethares W A. Measures of generalized magnitude-squared coherence: Differences and similarities. J Frankl Inst-Eng Appl Math, 2018, 355(5): 2932-2950.

1. Wang G, Wang D, Du C, et al. Seizure prediction using directed transfer function and convolution neural network on intracranial EEG. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(12): 2711-2720.
2. Li Y, Shi W, Liu Z, et al. Effective brain state estimation during propofol-induced sedation using advanced EEG microstate spectral analysis. IEEE J Biomed Health Inform, 2020, 25(4): 978-987.
3. Sivathamboo S, Farrand S, Chen Z, et al. Sleep-disordered breathing among patients admitted for inpatient video-EEG monitoring. Neurology, 2019, 92(3): e194-e204.
4. Gong R, Wegscheider M, Mühlberg C, et al. Spatiotemporal features of β-γ phase-amplitude coupling in Parkinson’s disease derived from scalp EEG. Brain, 2021, 144(2): 487-503.
5. Wen D, Fan Y, Hsu S H, et al. Combining brain–computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review. Ann Phys Rehabil Med, 2021, 64(1): 101404.
6. Urigüen J A, Garcia Z B. EEG artifact removal—state-of-the-art and guidelines. J Neural Eng, 2015, 12(3): 031001.
7. Egambaram A, Badruddin N, Asirvadam V S, et al. FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram. Biomed Signal Process Control, 2020, 57: 101692.
8. Sun W, Su Y, Wu X, et al. A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals. Neurocomputing, 2020, 404: 108-121.
9. Fatourechi M, Bashashati A, Ward R K, et al. EMG and EOG artifacts in brain computer interface systems: A survey. Clin Neurophysiol, 2007, 118(3): 480-494.
10. Klados M A, Papadelis C, Braun C, et al. REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts. Biomed Signal Process Control, 2011, 6(3): 291-300.
11. Islam M K, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiol Clin, 2016, 46(4-5): 287-305.
12. Chen Y, Zhao Q, Hu B, et al. A method of removing ocular artifacts from EEG using discrete wavelet transform and Kalman filtering// 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Shenzhen: IEEE, 2016: 1485-1492.
13. Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal, 2009, 1(01): 1-41.
14. 杨磊, 杨帆, 何艳. 采用样本熵-完备经验模态分解的脑电信号眼电伪迹去除算法. 西安交通大学学报, 2020, 54(8): 177-184.
15. Guarnieri R, Marino M, Barban F, et al. Online EEG artifact removal for BCI applications by adaptive spatial filtering. J Neural Eng, 2018, 15(5): 056009.
16. Burger C, van den Heever D J. Removal of EOG artefacts by combining wavelet neural network and independent component analysis. Biomed Signal Process Control, 2015, 15: 67-79.
17. Wang Z Y, Xu P, Liu T J, et al. Robust removal of ocular artifacts by combining independent component analysis and system identification. Biomed Signal Process Control, 2014, 10(3): 250-259.
18. Castellanos N P, Makarov V A. Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods, 2006, 158(2): 300-312.
19. Pion-Tonachini L, Hsu S H, Chang C Y, et al. Online automatic artifact rejection using the real-time EEG source-mapping toolbox (REST)// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 106-109.
20. Issa M F, Juhasz Z. Improved EOG artifact removal using wavelet enhanced independent component analysis. Brain Sci, 2019, 9(12): 355.
21. Durka P J, Klekowicz H, Blinowska K J, et al. A simple system for detection of EEG artifacts in polysomnographic recordings. IEEE Trans Biomed Eng, 2003, 50(4): 526-528.
22. Trigui O, Daoud S, Ghorbel M, et al. Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection. Signal Image Video Process, 2022, 16: 19-27.
23. Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage, 2007, 34(4): 1443-1449.
24. Mammone N, La Foresta F, Morabito F C. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sens J, 2011, 12(3): 533-542.
25. Teng C L, Zhang Y Y, Wang W, et al. A novel method based on combination of independent component analysis and ensemble empirical mode decomposition for removing electrooculogram artifacts from multichannel electroencephalogram signals. Front Neurosci, 2021, 15: 729403.
26. Klados M A, Bamidis P D. A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief, 2016, 8: 1004-1006.
27. 张锐, 刘家俊, 陈明明, 等. 基于小波变换-集合经验模态分解的单通道脑电信号眼电伪迹自动去除研究. 生物医学工程学杂志, 2021, 38(3): 473-482.
28. Majmudar C A, Mahajan R, Morshed B I. Real-time hybrid ocular artifact detection and removal for single channel EEG// 2015 IEEE International Conference on Electro/Information Technology (EIT). Dekalb: IEEE, 2015: 330-334.
29. Mahajan R, Morshed B I. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE J Biomed Health Inform, 2014, 19(1): 158-165.
30. Khatun S, Mahajan R, Morshed B I. Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data. IEEE J Transl Eng Health Med, 2016, 4: 1-8.
31. Malekpour S, Gubner J A, Sethares W A. Measures of generalized magnitude-squared coherence: Differences and similarities. J Frankl Inst-Eng Appl Math, 2018, 355(5): 2932-2950.

Journal of Biomedical Engineering

Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis

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