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Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings
Abstract
OBJECTIVE: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. METHODS: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. RESULTS: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. CONCLUSIONS: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. SIGNIFICANCE: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.
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Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
The ten-twenty electrode system of the international federation
Jasper, H. H. (1958) · Electroencephalography and Clinical Neurophysiology · Cited by 6,567
Removing electroencephalographic artifacts by blind source separation
Jung, T., Makeig, S., Humphries, C., Lee, T.-W., McKeown, M. J., Iragui, V. J., & Sejnowski, T. J. (2000) · Psychophysiology · doi:10.1017/s0048577200980259 · Cited by 3,334
Self-citationICLabel: An automated electroencephalographic independent component classifier, dataset, and website
Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.026 · Cited by 2,267
Self-citationLee, T.-W., Girolami, M., & Sejnowski, T. J. (1999) · Neural Computation · doi:10.1162/089976699300016719 · Cited by 1,967
EEG artifact removal—state-of-the-art and guidelines
Urigüen, J. A., & Garcia-Zapirain, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/031001 · Cited by 963
Independent EEG Sources Are Dipolar
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030135 · Cited by 810
Real-time neuroimaging and cognitive monitoring using wearable dry EEG
Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T.-P., & Cauwenberghs, G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2481482 · Cited by 795
Self-citationImaging human EEG dynamics using independent component analysis
Onton, J., Westerfield, M., Townsend, J., & Makeig, S. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.06.007 · Cited by 673
Autoreject: Automated artifact rejection for MEG and EEG data
Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.030 · Cited by 598
Gabard-Durnam, L. J., Mendez Leal, A. S., Wilkinson, C. L., & Levin, A. R. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00097 · Cited by 535
Marzbani, H., Marateb, H., & Mansourian, M. (2016) · Basic and Clinical Neuroscience Journal · doi:10.15412/j.bcn.03070208 · Cited by 484
Methods for artifact detection and removal from scalp EEG: A review
Islam, M. K., Rastegarnia, A., & Yang, Z. (2016) · Neurophysiologie Clinique/Clinical Neurophysiology · doi:10.1016/j.neucli.2016.07.002 · Cited by 457
BCILAB: a platform for brain–computer interface development
Kothe, C. A., & Makeig, S. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/5/056014 · Cited by 412
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal
Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P. (2018) · 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512547 · Cited by 335
Self-citationRobust artifactual independent component classification for BCI practitioners
Winkler, I., Brandl, S., Horn, F., Waldburger, E., Allefeld, C., & Tangermann, M. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/035013 · Cited by 327
Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)
Fabiani, G. E., McFarland, D. J., Wolpaw, J. R., & Pfurtscheller, G. (2004) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2004.834627 · Cited by 292
A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling
Blum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00141 · Cited by 186
A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements
Kilicarslan, A., Grossman, R. G., & Contreras-Vidal, J. L. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/2/026013 · Cited by 184
Artoni, F., Fanciullacci, C., Bertolucci, F., Panarese, A., Makeig, S., Micera, S., & Chisari, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.013 · Cited by 183
Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features
Radüntz, T., Scouten, J., Hochmuth, O., & Meffert, B. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa69d1 · Cited by 155
The localization of equivalent dipoles of EEG sources by the application of electrical field theory
Henderson, C. J., Butler, S. R., & Glass, A. (1975) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(75)90002-4 · Cited by 150
Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution
Bulea, T. C., Prasad, S., Kilicarslan, A., & Contreras-Vidal, J. L. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00376 · Cited by 133
Separation of Sources From Single-Channel EEG Signals Using Independent Component Analysis
Maddirala, A. K., & Ahamed, S. R. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2775358 · Cited by 124
Gao, J., Zheng, C., & Wang, P. (2010) · Clinical EEG and Neuroscience · doi:10.1177/155005941004100111 · Cited by 110
Tonic Changes in EEG Power Spectra during Simulated Driving
Huang, R., Jung, T., & Makeig, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02812-0_47 · Cited by 87
Self-citationHu, J., Wang, C., Wu, M., Du, Y., He, Y. M., & She, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.09.040 · Cited by 84
Bigdely-Shamlo, N., Kreutz-Delgado, K., Kothe, C. A., & Makeig, S. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610881 · Cited by 79
Online Removal of Eye Movement and Blink EEG Artifacts Using a High-Speed Eye Tracker
Noureddin, B., Lawrence, P. D., & Birch, G. E. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2108295 · Cited by 79
Classification of independent components of EEG into multiple artifact classes
Frølich, L., Andersen, T., & Mørup, M. (2014) · Psychophysiology · doi:10.1111/psyp.12290 · Cited by 56
Online EEG artifact removal for BCI applications by adaptive spatial filtering
Guarnieri, R., Marino, M., Barban, F., Ganzetti, M., & Mantini, D. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aacfdf · Cited by 53
Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information
Zhang, C., Li, T., Zeng, Y., Jiang, J., Bu, H., Yan, B., & Li, J. (2015) · BioMed Research International · doi:10.1155/2015/720450 · Cited by 42
Removal of High-Voltage Brain Stimulation Artifacts From Simultaneous EEG Recordings
Lee, S., McKeown, M. J., Wang, Z. J., & Chen, X. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2828808 · Cited by 40
Real-time EEG Source-mapping Toolbox (REST): Online ICA and source localization
Pion-Tonachini, L., Hsu, S.-H., Makeig, S., Jung, T., & Cauwenberghs, G. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319299 · Cited by 32
Self-citationAlain De Cheveigné, A. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.01.005 · Cited by 24
A Review of Electroencephalogram-Based Analysis and Classification Frameworks for Dyslexia
Perera, H., Shiratuddin, M. F., & Wong, K. W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46681-1_74 · Cited by 18
Grummett, T. S., Fitzgibbon, S. P., Lewis, T. W., DeLosAngeles, D., Whitham, E. M., Pope, K. J., & Willoughby, J. O. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00927 · Cited by 14
Ojeda, A., Klug, M., Kreutz-Delgado, K., Gramann, K., & Mishra, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/559450 · Cited by 13
Cited by
Klug, M., & Gramann, K. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.14992 · Cited by 268
Chuang, C.-H., Chang, K.-Y., Huang, C.-S., & Jung, T.-P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119586 · Cited by 88
Self-citationBilleci, L., Callara, A. L., Guiducci, L., Prosperi, M., Morales, M. A., Calderoni, S., Muratori, F., & Santocchi, E. (2023) · Autism · doi:10.1177/13623613221082710 · Cited by 69
A data augmentation scheme for geometric deep learning in personalized brain–computer interfaces.
Kalaganis, F. P., Laskaris, N. A., Chatzilari, E., Nikolopoulos, S., & Kompatsiaris, I. (2020) · IEEE Access · doi:10.1109/access.2020.3021580 · Cited by 26
The effect of alpha neurofeedback training on cognitive performance in healthy adults
Nawaz, R., Nisar, H., Yap, V. V., & Tsai, C.-Y. (2022) · Mathematics · doi:10.3390/math10071095 · Cited by 25
“After that, an automatic method known as artifact subspace reconstruction (ASR) is used to reconstruct the artifacts portion of the data with clean data [44].”
Metadata
- Authors
- Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2020-04
- Volume
- 67
- Issue
- 4
- Pages
- 1114-1121
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2963881755
- Cited by
- 544
- References
- 40
- FWCI
- 25.01
- Type
- journal article
- Language
- en
- Open access
- closed
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