Study of denoising of simultaneous electroencephalogram-functional magnetic resonance imaging signal based on real-time constrained independent components analysis_Journal of Biomedical Engineering

Authors：

WANG Kai ^1,2 , YAN Hanying ^1,2 ,  ZOU Ling ^1,2

1. School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R.China;
2. Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164, P.R.China;

Corresponding author：

ZOU Ling, Email: zouling@cczu.edu.cn

Keywords：

electroencephalogram-functional magnetic resonance imaging; ballistocardiogram artifacts; constrained independent components analysis; artifacts removal

DOI：

10.7507/1001-5515.201709066

Video：

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Abstract Full text Figures/Tables Video References Cited by

Simultaneous recording of electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) plays an important role in scientific research and clinical field due to its high spatial and temporal resolution. However, the fusion results are seriously influenced by ballistocardiogram (BCG) artifacts under MRI environment. In this paper, we improve the off-line constrained independent components analysis using real-time technique (rt-cICA), which is applied to the simulated and real resting-state EEG data. The results show that for simulated data analysis, the value of error in signal amplitude (Er) obtained by rt-cICA method was obviously lower than the traditional methods such as average artifact subtraction (P<0.005). In real EEG data analysis, the improvement of normalized power spectrum (INPS) calculated by rt-cICA method was much higher than other methods (P<0.005). In conclusion, the novel method proposed by this paper lays the technical foundation for further research on the fusion model of EEG-fMRI.

Citation： WANG Kai, YAN Hanying, ZOU Ling. Study of denoising of simultaneous electroencephalogram-functional magnetic resonance imaging signal based on real-time constrained independent components analysis. Journal of Biomedical Engineering, 2019, 36(1): 7-15, 23. doi: 10.7507/1001-5515.201709066 Copy

1.	Guo Qian, Zhou Tiantong, Li Wenjie, et al. Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function. Brain Behav, 2017, 7(7): e00728.
2.	邹凌, 严永, 杨彪, 等. 基于同步 EEG-fMRI 采集的情绪认知重评数据特征融合分析研究. 自动化学报, 2016, 42(5): 771-781.
3.	李静, 王金甲, 李慧. 融合脑电特征的弹性网特征选择和分类. 生物医学工程学杂志, 2016(3): 413-419.
4.	Allen P J, Josephs O, Turner R. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage, 2000, 12(2): 230-239.
5.	Mullinger K J, Havenhand J, Bowtell R. Identifying the sources of the pulse artefact in EEG recordings made inside an MR scanner. Neuroimage, 2013, 71: 75-83.
6.	Mayeli A, Zotev V, Refai H, et al. Real-time EEG artifact correction during fMRI using ICA. J Neurosci Methods, 2016, 274: 27-37.
7.	Grouiller F, Vercueil L, Krainik A A, et al. A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage, 2007, 38(1): 124-137.
8.	Leclercq Y, Balteau E, Dang-Vu T, et al. Rejection of pulse related artefact (PRA) from continuous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA). NeuroImage, 2009, 44(3): 679-691.
9.	Niazy R K, Beckmann C F, Iannetti G D, et al. Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage, 2005, 28(3): 720-737.
10.	Krishnaswamy P, Bonmassar G, Poulsen C A, et al. Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression. Neuroimage, 2016, 128: 398-412.
11.	Abolghasemi V, Ferdowsi S. EEG-fMRI: Dictionary learning for removal of ballistocardiogram artifact from EEG. Biomed Signal Process Control, 2015, 18: 186-194.
12.	Wu X, Wu T, Zhan Z, et al. A real-time method to reduce ballistocardiogram artifacts from EEG during fMRI based on optimal basis sets (OBS). Computer Methods and Programs in Biomedicine, 2016, 127: 114-125.
13.	Xia Hongjing, Ruan Dan, Cohen M S. Separation and reconstruction of BCG and EEG signals during continuous EEG and fMRI recordings. Front Neurosci, 2014, 8(8): 163.
14.	Abreu R, Leite M, Jorge J, et al. Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI. Neuroimage, 2016, 135: 45-63.
15.	Yao D Z. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas, 2001, 22(4): 693-711.
16.	Christov I I. Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online, 2004, 3(1): 1-9.

1. Guo Qian, Zhou Tiantong, Li Wenjie, et al. Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function. Brain Behav, 2017, 7(7): e00728.
2. 邹凌, 严永, 杨彪, 等. 基于同步 EEG-fMRI 采集的情绪认知重评数据特征融合分析研究. 自动化学报, 2016, 42(5): 771-781.
3. 李静, 王金甲, 李慧. 融合脑电特征的弹性网特征选择和分类. 生物医学工程学杂志, 2016(3): 413-419.
4. Allen P J, Josephs O, Turner R. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage, 2000, 12(2): 230-239.
5. Mullinger K J, Havenhand J, Bowtell R. Identifying the sources of the pulse artefact in EEG recordings made inside an MR scanner. Neuroimage, 2013, 71: 75-83.
6. Mayeli A, Zotev V, Refai H, et al. Real-time EEG artifact correction during fMRI using ICA. J Neurosci Methods, 2016, 274: 27-37.
7. Grouiller F, Vercueil L, Krainik A A, et al. A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage, 2007, 38(1): 124-137.
8. Leclercq Y, Balteau E, Dang-Vu T, et al. Rejection of pulse related artefact (PRA) from continuous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA). NeuroImage, 2009, 44(3): 679-691.
9. Niazy R K, Beckmann C F, Iannetti G D, et al. Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage, 2005, 28(3): 720-737.
10. Krishnaswamy P, Bonmassar G, Poulsen C A, et al. Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression. Neuroimage, 2016, 128: 398-412.
11. Abolghasemi V, Ferdowsi S. EEG-fMRI: Dictionary learning for removal of ballistocardiogram artifact from EEG. Biomed Signal Process Control, 2015, 18: 186-194.
12. Wu X, Wu T, Zhan Z, et al. A real-time method to reduce ballistocardiogram artifacts from EEG during fMRI based on optimal basis sets (OBS). Computer Methods and Programs in Biomedicine, 2016, 127: 114-125.
13. Xia Hongjing, Ruan Dan, Cohen M S. Separation and reconstruction of BCG and EEG signals during continuous EEG and fMRI recordings. Front Neurosci, 2014, 8(8): 163.
14. Abreu R, Leite M, Jorge J, et al. Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI. Neuroimage, 2016, 135: 45-63.
15. Yao D Z. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas, 2001, 22(4): 693-711.
16. Christov I I. Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online, 2004, 3(1): 1-9.

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