The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing
Open accessAbstract
A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.
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“Applying the artifact removal algorithm FORCe [73] effectively eliminates this component (S1 Fig).”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [73]
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1 retracted
RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis
Chawla, M. P. S., Verma, H., & Kumar, V. (2007) · International Journal of Cardiology · doi:10.1016/j.ijcard.2007.06.037 · Cited by 54
RetractedControlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Independent component analysis: algorithms and applications
Hyvärinen, A., & Erkki Oja, A. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00026-5 · Cited by 8,926
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
A blind source separation technique using second-order statistics
Belouchrani, A., Abed‐Meraim, K., Cardoso, J.-F., & Moulines, É. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.554307 · Cited by 2,774
Nunez, P. L., & Srinivasan, R. (2006) · doi:10.1093/acprof:oso/9780195050387.001.0001 · Cited by 2,531
Electric Fields of the Brain: The Neurophysics of EEG
Nunez, P. L., & Harth, E. M. (1982) · Physics Today · doi:10.1063/1.2915137 · Cited by 2,257
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Research works printed but not linked: 2
- ziehe, 1998, TDSEP?An efficient algorithm for blind source separation using time structure, Proc Int Conf Artificial Neural Networks, 675
- niedermeyer, 1999, The normal EEG of the waking adult, Electroencephalography Basic principles clinical application and related fields, 149
Cited by
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Removal of Artifacts from EEG Signals: A Review
Xiao, J., Bian, G., & Tian, Z. (2019) · Sensors · doi:10.3390/s19050987 · Cited by 900
Methods for artifact detection and removal from scalp EEG: A review
Islam, M. K., Rastegarnia, A., & Yang, Z. Q. (2016) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2016.07.002 · Cited by 457
A multimodal approach to estimating vigilance using EEG and forehead EOG
Zheng, W.-L., & Lu, B.-L. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a98 · Cited by 424
Hong, K.-S., & Khan, M. J. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00035 · Cited by 267
Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review
Mannan, M. M. N., Kamran, M. A., & Jeong, M. Y. (2018) · IEEE Access · doi:10.1109/access.2018.2842082 · Cited by 224
Metadata
- Authors
- Daly, I., Scherer, R., Billinger, M., & Muller-Putz, G.
- Affiliations
- Graz University of Technology (AT)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2015-09
- Volume
- 23
- Issue
- 5
- Pages
- 725-736
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2005521760
- Cited by
- 174
- References
- 43
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 9 · 2023: 14 · 2022: 12 · 2021: 17 · 2020: 21 · 2019: 25 · 2018: 24 · 2017: 18 · 2016: 15 · 2015: 7
- FWCI
- 7.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Open Access at Essex (University of Essex) (acceptedVersion)
- Funders
- FP7 Framework EU Research Project ABC (287774)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- EEG artifact removal, brain-computer interface, wavelet decomposition, independent component analysis, thresholding, blink artifacts, cerebral palsy
- MeSH terms
- Algorithms, Brain, Cerebral Palsy, Electroencephalography, Evoked Potentials, Female, Humans, Male, Online Systems, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Software, Reproducibility of Results, Artifacts, Internet, Principal Component Analysis, Wavelet Analysis, Brain-Computer Interfaces
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