Sparsity-based correction of exponential artifacts

Signal Processing

Volumn 120, Issue , 2016, Pages 236-248

  Ding, Yin ^a   Selesnick, Ivan W ^a  

^a POLYTECHNIC UNIVERSITY   (United States)

Author keywords

Artifact removal; Signal decomposition; Sparsity

Indexed keywords

ALGORITHMS; CONVEX OPTIMIZATION; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; OPTIMIZATION; SIGNAL PROCESSING;

ARTIFACT REMOVAL; CONVEX OPTIMIZATION PROBLEMS; ELECTROCORTICOGRAPHY (ECOG); MEASUREMENT ARTIFACTS; PIECEWISE SMOOTH; SIGNAL DECOMPOSITION; SPARSITY; SYNTHETIC SIGNALS;

TIME SERIES ANALYSIS;

EID: 84944252309     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2015.09.017     Document Type: Article

References (69)

1. M.T. Akhtar, W. Mitsuhashi, and C.J. James Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data Signal Process. 92 2 2012 401 416
2. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski Optimization with sparsity-inducing penalties Found. Trends Mach. Learn. 4 1 2012 1 106
3. A. Beck, and M. Teboulle A fast iterative shrinkage-thresholding algorithm for linear inverse problems SIAM J. Imag. Sci. 2 1 2009 183 202
4. E.J. Candès, M.B. Wakin, and S. Boyd Enhancing sparsity by reweighted l1 minimization J. Fourier Anal. Appl. 14 December (5) 2008 877 905
5. M. Cetin, and W.C. Karl Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization IEEE Trans. Image Process. 10 April (4) 2001 623 631
6. A. Chambolle, R.A. de Vore, N.-Y. Lee, and B.J. Lucier Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage IEEE Trans. Image Process. 7 March (3) 1998 319 335
7. A. Chambolle, V.R. Dossal, On the convergence of the iterates of FISTA, hal-01060130, September 2014, preprint.
8. C.-P. Chang, J.-C. Lee, Y. Su, P.S. Huang, and T.-M. Tu Using empirical mode decomposition for iris recognition Comput. Stand. Interfaces 31 4 2009 729 739
9. H.-C. Chang, P.-L. Lee, M.-T. Lo, Y.-T. Wu, K.-W. Wang, and G.-Y. Lan Inter-trial analysis of post-movement beta activities in EEG signals using multivariate empirical mode decomposition IEEE Trans. Neural Syst. Rehabil. Eng. 21 July (4) 2013 607 615
10. P.-Y. Chen, and I.W. Selesnick Group-sparse signal denoising: non-convex regularization, convex optimization IEEE Trans. Signal Process. 62 July (13) 2014 3464 3478
11. R.R. Coifman, D.L. Donoho, Translation-invariant denoising, in: A. Antoniadis (Ed.), Lecture Notes in Wavelets and Statistics, Springer-Verlag, 1995.
12. J. Dammers, M. Schiek, F. Boers, C. Silex, M. Zvyagintsev, U. Pietrzyk, and K. Mathiak Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings IEEE Trans. Biomed. Eng. 55 October (10) 2008 2353 2362
13. S. Durand, J. Froment, Artifact free signal denoising with wavelets, in: Proceedings of ICASSP, 2001.
14. S. Durand, and J. Froment Reconstruction of wavelet coefficients using total variation minimization SIAM J. Sci. Comput. 24 5 2003 1754 1767
15. M. Figueiredo, J. Bioucas-Dias, and R. Nowak Majorization-minimization algorithms for wavelet-based image restoration IEEE Trans. Image Process. 16 December (12) 2007 2980 2991
16. J. Fleureau, A. Kachenoura, L. Albera, J.-C. Nunes, and L. Senhadji Multivariate empirical mode decomposition and application to multichannel filtering Signal Process. 91 12 2011 2783 2792
17. J.-J. Fuchs On sparse representations in arbitrary redundant bases IEEE Trans. Inf. Theory 50 6 2004 1341 1344
18. H. Gao Wavelet shrinkage denoising using the non-negative garrote J. Comput. Graph. Stat. 7 4 1998 469 488
19. A. Gholami, and S.M. Hosseini A balanced combination of Tikhonov and total variation regularizations for reconstruction of piecewise-smooth signals Signal Process. 93 7 2013 1945 1960
20. C. Guerrero-Mosquera, and A. Navia-Vázquez Automatic removal of ocular artifacts using adaptive filtering and independent component analysis for electroencephalogram data IET Signal Process. 6 2 2012 99 106
21. H. Huang, and J. Pan Speech pitch determination based on Hilbert-Huang transform Signal Process. 86 April (4) 2006 792 803
22. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A, 454 (March) (1998) 903-995.
23. N.E. Huang, M.-L.C. Wu, S.R. Long, S.S.P. Shen, W. Qu, P. Gloersen, K.L. Fan, A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. R. Soc. Lond. Ser. A: Math. Phys. Eng. Sci. 459 (September (2037)) (2003) 2317-2345.
24. D.R. Hunter, and K. Lange A tutorial on MM algorithms Am. Stat. 58 2004 30 37
25. M.K. Islam, A. Rastegarnia, A.T. Nguyen, and Z. Yang Artifact characterization and removal for in vivo neural recording J. Neurosci. Methods 226 0 2014 110 123
26. C.A. Joyce, I.F. Gorodnitsky, and M. Kutas Automatic removal of eye movement and blink artifacts from EEG data using blind component separation Psychophysiology 41 2 2004 313 325
27. F.I. Karahanoglu, I. Bayram, and D. Van De Ville A signal processing approach to generalized 1-d total variation IEEE Trans. Signal Process. 59 November (11) 2011 5265 5274
28. E. Kilic Explicit formula for the inverse of a tridiagonal matrix by backward continued fractions Appl. Math. Comput. 197 1 2008 345 357
29. E. Kilic, and P. Stanica The inverse of banded matrices J. Comput. Appl. Math. 237 1 2013 126 135
30. 1 trend filtering SIAM Rev. 51 2 2009 339 360
31. Y. Kopsinis, S. McLaughlin, Empirical mode decomposition based soft-thresholding, in: 16th European Signal Processing Conference, 2008.
32. Y. Kopsinis, and S. McLaughlin Development of EMD-based denoising methods inspired by wavelet thresholding IEEE Trans. Signal Process. 57 April (4) 2009 1351 1362
33. M. Kowalski Sparse regression using mixed norms Appl. Comput. Harmonic Anal. 27 3 2009 303 324
34. I. Kozlov, and A. Petukhov Sparse solutions of underdetermined linear systems W. Freeden, Handbook of Geomathematics 2010 Springer
35. K. Lange Optimization 2004 Springer New York
36. K. Lange, D. Hunter, and I. Yang Optimization transfer using surrogate objective functions J. Comput. Graph. Stat. 9 2000 1 20
37. N. Mammone, F. L a Foresta, and F.C. Morabito Automatic artifact rejection from multichannel scalp EEG by wavelet ICA IEEE J. Sens. 12 March (3) 2012 533 542
38. D. Mandic, Empirical mode decomposition, multivariate EMD, matlab and data sources.
39. D.P. Mandic, N.U. Rehman, Z. Wu, and N.E. Huang Empirical mode decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis IEEE Signal Process. Mag. 30 November (6) 2013 74 86
40. B. Molavi, and G.A. Dumont Wavelet-based motion artifact removal for functional near-infrared spectroscopy Physiol. Meas. 33 2 2012 259
41. M.K.I. Molla, M.R. Islam, T. Tanaka, and T.M. Rutkowski Artifact suppression from EEG signals using data adaptive time domain filtering Neurocomputing 97 2012 297 308
42. M.K.I. Molla, T. Tanaka, T.M. Rutkowski, Multivariate EMD based approach to EOG artifacts separation from EEG, in: 2012 Proceedings of ICASSP, 2012, pp. 653-656.
43. M.K.I. Molla, T. Tanaka, T.M. Rutkowski, A. Cichocki, Separation of EOG artifacts from EEG signals using bivariate EMD, in: 2010 Proceedings of ICASSP, 2010, pp. 562-565.
44. K. Nazarpour, Y. Wongsawat, S. Sanei, J.A. Chambers, and S. Oraintara Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming IEEE Trans. Biomed. Eng. 55 9 2008 2221 2231
45. D. Needell, Noisy signal recovery via iterative reweighted l1-minimization, in: Proceedings of Forty-Third Asilomar Conference on Signals, Systems and Computers, 2009, pp. 113-117.
46. M. Nikolova Analysis of the recovery of edges in images and signals by minimizing nonconvex regularized least-squares Multiscale Model. Simul. 4 3 2005 960 991
47. X. Ning, and I.W. Selesnick ECG enhancement and QRS detection based on sparse derivatives Biomed. Signal Process. Control 8 6 2013 713 723
48. B. Noureddin, P.D. Lawrence, and G.E. Birch Online removal of eye movement and blink EEG artifacts using a high-speed eye tracker IEEE Trans. Biomed. Eng. 59 8 2012 2103 2110
49. A. Omidvarnia, G. Azemi, P.B. Colditz, and B. Boashash A time-frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals Digit. Signal Process. 23 3 2013 780 790
50. C. Park, M. Plank, J. Snider, S. Kim, H.C. Huang, S. Gepshtein, T.P. Coleman, and H. Poizner EEG gamma band oscillations differentiate the planning of spatially directed movements of the arm versus eye: multivariate empirical mode decomposition analysis IEEE Trans. Neural Syst. Rehabil. Eng. 22 September (5) 2014 1083 1096
51. R.M. Rangayyan, Biomedical Signal Analysis - A Case-study Approach, IEEE and Wiley, New York, NY, 2002.
52. N.U. Rehman, D.P. Mandic, Multivariate empirical mode decomposition, Proc. R. Soc. A, 466 (2117) (2010) 1291-1302.
53. N.U. Rehman, and D.P. Mandic Filter bank property of multivariate empirical mode decomposition IEEE Trans. Signal Process. 59 May (5) 2011 2421 2426
54. G. Rilling, and P. Flandrin One or two frequencies? The empirical mode decomposition answers IEEE Trans. Signal Process. 56 1 2008 85 95
55. W. Rudin Principles of Mathematical Analysis 1976 McGraw-Hill
56. H. Sato, N. Tanaka, M. Uchida, Y. Hirabayashi, M. Kanai, T. Ashida, I. Konishi, and A. Maki Wavelet analysis for detecting body-movement artifacts in optical topography signals NeuroImage 33 2 2006 580 587
57. E.D. Schifano, R.L. Strawderman, and M.T. Wells Majorization-minimization algorithms for nonsmoothly penalized objective functions Electron. J. Stat. 4 2010 1258 1299
58. I.W. Selesnick, S. Arnold, and V. Dantham Polynomial smoothing of time series with additive step discontinuities IEEE Trans. Signal Process. 60 December (12) 2012 6305 6318
59. I.W. Selesnick, and I. Bayram Sparse signal estimation by maximally sparse convex optimization IEEE Trans. Signal Process. 62 March (5) 2014 1078 1092
60. I.W. Selesnick, H.L. Graber, Y. Ding, T. Zhang, and R.L. Barbour Transient artifact reduction algorithm (TARA) based on sparse optimization IEEE Trans. Signal Process. 62 December (24) 2014 6596 6611
61. I.W. Selesnick, H.L. Graber, S. Douglas, S. Pfeil, and R.L. Barbour Simultaneous low-pass filtering and total variation denoising IEEE Trans. Signal Process. 62 March (5) 2014 1109 1124
62. B. Tang, S. Dong, and T. Song Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation Signal Process. 92 1 2012 248 258
63. 2 methods for finding sparse solutions IEEE. J. Sel. Top. Signal Process. 4 April (2) 2010 317 329
64. Y. Wongsawat, Efficient implementation of RMVB for eyeblink artifacts removal of EEG via STF-TS modeling, in: 2008 Proceedings of ROBIO, 2008, pp. 1567-1572.
65. X. Xie Illumination preprocessing for face images based on empirical mode decomposition Signal Process. 103 0 2014 250 257
66. M. Yaghoobi, T. Blumensath, and M.E. Davies Dictionary learning for sparse approximations with the majorization method IEEE Trans. Signal Process. 57 June (6) 2009 2178 2191
67. J. Yan, and L. Lu Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis Signal Process. 98 2014 74 87
68. S. Yu, A.S. Khwaja, and J. Ma Compressed sensing of complex-valued data Signal Process. 92 2 2012 357 362
69. H. Zeng, A. Song, R. Yan, and H. Qin EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition Sensors 13 11 2013 14839 14859