Abstract
Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG–fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.
Similar content being viewed by others
References
Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12:230–239
Allen PJ, Polizzi G, Krakow K, Fish DR, Lemieux L (1998) Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction. Neuroimage 8:229–239
Aspinall P, Mavros P, Coyne R, Roe J (2015) The urban brain: analysing outdoor physical activity with mobile EEG. Br J Sports Med 49:272–276
Crespo-Garcia M, Atienza M, Cantero JL (2008) Muscle artifact removal from human sleep EEG by using independent component analysis. Ann Biomed Eng 36:467–475
De Clercq W, Vergult A, Vanrumste B, Van Paesschen W, Van Huffel S (2006) Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng 53:2583–2587
De Munck JC, Van Houdt PJ, Goncalves SI, Van Wegen E, Ossenblok PP (2013) Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction. Neuroimage 64:407–415
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21
Dissanayaka C, Ben-Simon E, Gruberger M, Maron-Katz A, Sharon H, Hendler T, Cvetkovic D (2015) Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods. Med Biol Eng Comput 53:599–607
Goncalves SI, Pouwels PJ, Kuijer JP, Heethaar RM, De Munck JC (2007) Artifact removal in co-registered EEG/fMRI by selective average subtraction. Clin Neurophysiol 118:2437–2450
Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahane P, David O (2007) A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage 38:124–137
He B, Liu Z (2008) Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG. IEEE Rev Biomed Eng 1:23–40
Hoffmann A, Jäger L, Werhahn K, Jaschke M, Noachtar S, Reiser M (2000) Electroencephalography during functional echo-planar imaging: detection of epileptic spikes using post-processing methods. Magn Reson Med 44:791–798
Hotelling H (1936) Relations between two sets of variates. Biometrika 28:321–377
Iannaccone R, Hauser TU, Staempfli P, Walitza S, Brandeis D, Brem S (2015) Conflict monitoring and error processing: new insights from simultaneous EEG–fMRI. Neuroimage 105:395–407
Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37:163–178
Liu Z, de Zwart JA, van Gelderen P, Kuo LW, Duyn JH (2012) Statistical feature extraction for artifact removal from concurrent fMRI–EEG recordings. Neuroimage 59:2073–2087
Mandelkow H, Brandeis D, Boesiger P (2010) Good practices in EEG–MRI: the utility of retrospective synchronization and PCA for the removal of MRI gradient artefacts. Neuroimage 49:2287–2303
Mantini D, Perrucci MG, Cugini S, Ferretti A, Romani GL, Del Gratta C (2007) Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis. Neuroimage 34:598–607
Negishi M, Abildgaard M, Nixon T, Constable RT (2004) Removal of time-varying gradient artifacts from EEG data acquired during continuous fMRI. Clin Neurophysiol 115:2181–2192
Niazy RK, Beckmann CF, Iannetti GD, Brady JM, Smith SM (2005) Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28:720–737
Ritter P, Villringer A (2006) Simultaneous EEG–fMRI. Neurosci Biobehav Rev 30:823–838
Vecchiato G, Borghini G, Aricò P, Graziani I, Maglione AG, Cherubino P, Babiloni F (2016) Investigation of the effect of EEG-BCI on the simultaneous execution of flight simulation and attentional tasks. Med Biol Eng Comput 54:1503–1513
Zotev V, Phillips R, Yuan H, Misaki M, Bodurka J (2014) Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. Neuroimage 85(Pt 3):985–995
Acknowledgements
The authors thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under Grant R-719-001-102-232. This work was also partially supported by the Ministry of Education of Singapore under the Grant MOE2014-T2-1-115.
Author information
Authors and Affiliations
Corresponding authors
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Li, J., Chen, Y., Taya, F. et al. A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal. Med Biol Eng Comput 55, 1669–1681 (2017). https://doi.org/10.1007/s11517-017-1620-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-017-1620-3