WQN algorithm for EEG artifact removal in the absence of scale invariance
The codes are used to generate the results and the figures associated with the following publication: M. Dora, S. Jaffard, D. Holcman. The WQN algorithm for EEG artifact removal in the absence of scale invariance, (to appear in) IEEE Transactions on Signal Processing 2024 EEG, EMG, and EOG data used in the present article can be obtained from publicly available in References: B. Blankertz, G. Dornhege, M. Krauledat, K.-R. M ̈uller, G. Curio, The non-invasive berlin brain–computer interface: fast acquisition of effective performance in untrained subjects, NeuroImage 37 (2) (2007) 539–550. K. T. Sweeney, H. Ayaz, T. E. Ward, M. Izzetoglu, S. F.
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- zip
- Original repository
- 10938810
- DOI
- 10.5281/zenodo.10938810
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- Creators in Zenodo record metadata
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- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
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- cc-by-4.0
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- Nov 5, 2026
About this resource
The codes are used to generate the results and the figures associated with the following publication: M. Dora, S. Jaffard, D. Holcman. The WQN algorithm for EEG artifact removal in the absence of scale invariance, (to appear in) IEEE Transactions on Signal Processing 2024 EEG, EMG, and EOG data used in the present article can be obtained from publicly available in References: B. Blankertz, G. Dornhege, M. Krauledat, K.-R. M ̈uller, G. Curio, The non-invasive berlin brain–computer interface: fast acquisition of effective performance in untrained subjects, NeuroImage 37 (2) (2007) 539–550. K. T. Sweeney, H. Ayaz, T. E. Ward, M. Izzetoglu, S. F. McLoone, B. Onaral, A methodology for validating artifact removal techniques for physiological signals, IEEE transactions on information technology in biomedicine 16 (5) (2012) 918–926. A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, H. E. Stanley, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals, circulation 101 (23) (2000) e215–e220. S. F. Quan, B. V. Howard, C. Iber, J. P. Kiley, F. J. Nieto, G. T. O’Connor, D. M. Rapoport, S. Redline, J. Robbins, J. M. Samet, et al., The sleep heart health study: design, rationale, and methods, Sleep 20 (12) (1997) 1077–1085. Zhang, M. Zhao, C. Wei, D. Mantini, Z. Li, Q. Liu, Eegdenoisenet: A benchmark dataset for deep learning solutions of eeg denoising, arXiv preprint arXiv:2009.11662 (2020).