{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T02:47:11Z","timestamp":1773283631724,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2012,7,2]],"date-time":"2012-07-02T00:00:00Z","timestamp":1341187200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.<\/jats:p>","DOI":"10.1186\/1687-6180-2012-127","type":"journal-article","created":{"date-parts":[[2012,7,2]],"date-time":"2012-07-02T16:17:30Z","timestamp":1341245850000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":149,"title":["Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches"],"prefix":"10.1186","volume":"2012","author":[{"given":"Doha","family":"Safieddine","sequence":"first","affiliation":[]},{"given":"Amar","family":"Kachenoura","sequence":"additional","affiliation":[]},{"given":"Laurent","family":"Albera","sequence":"additional","affiliation":[]},{"given":"Gw\u00e9na\u00ebl","family":"Birot","sequence":"additional","affiliation":[]},{"given":"Ahmad","family":"Karfoul","sequence":"additional","affiliation":[]},{"given":"Anca","family":"Pasnicu","sequence":"additional","affiliation":[]},{"given":"Arnaud","family":"Biraben","sequence":"additional","affiliation":[]},{"given":"Fabrice","family":"Wendling","sequence":"additional","affiliation":[]},{"given":"Lotfi","family":"Senhadji","sequence":"additional","affiliation":[]},{"given":"Isabelle","family":"Merlet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,7,2]]},"reference":[{"issue":"1","key":"260_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.neuroimage.2010.07.057","volume":"54","author":"BW McMenamin","year":"2010","unstructured":"McMenamin BW, Shackman AJ, Greischar LL, Davidson RJ: Electromyogenic artifacts and electroencephalographic inferences revisited. NeuroImage 2010, 54(1):4-9.","journal-title":"NeuroImage"},{"issue":"12","key":"260_CR2","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1016\/j.clinph.2008.09.007","volume":"119","author":"M Congedo","year":"2008","unstructured":"Congedo M, Gouy-Pailler C, Jutten C: On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin. Neurophysiol. 2008, 119(12):2677-2686. 10.1016\/j.clinph.2008.09.007","journal-title":"Clin. Neurophysiol"},{"key":"260_CR3","volume-title":"Handbook of Blind Source Separation","author":"L Albera","year":"2010","unstructured":"Albera L, Comon P, Parra L, Karfoul A, Kachenoura A, Senhadji L: Handbook of Blind Source Separation. Edited by: Comon P, Jutten C. Academic Press, New York; 2010."},{"issue":"4","key":"260_CR4","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s00422-001-0298-6","volume":"86","author":"S Vorobyov","year":"2002","unstructured":"Vorobyov S, Cichocki A: Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biol. Cybern. 2002, 86(4):293-303. 10.1007\/s00422-001-0298-6","journal-title":"Biol. Cybern"},{"issue":"4","key":"260_CR5","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1097\/00004691-200307000-00004","volume":"20","author":"J Iriarte","year":"2003","unstructured":"Iriarte J, Urrestarazu E, Valencia M, Alegre M, Malanda A, Viteri C, Artieda J: Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J. Clin. Neurophysiol. 2003, 20(4):249-257. 10.1097\/00004691-200307000-00004","journal-title":"J. Clin. Neurophysiol"},{"issue":"6","key":"260_CR6","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1097\/01.wnp.0000236579.08698.23","volume":"23","author":"J Iriarte","year":"2006","unstructured":"Iriarte J, Urrestarazu E, Artieda J, Valencia M, Levan P, Viteri C, Alegre M: Independent component analysis in the study of focal seizures. J. Clin. Neurophysiol. 2006, 23(6):551-558. 10.1097\/01.wnp.0000236579.08698.23","journal-title":"J. Clin. Neurophysiol"},{"issue":"4","key":"260_CR7","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.clinph.2005.12.013","volume":"117","author":"P LeVan","year":"2006","unstructured":"LeVan P, Urrestarazu E, Gotman J: A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification. Clin. Neurophysiol. 2006, 117(4):912-927. 10.1016\/j.clinph.2005.12.013","journal-title":"Clin. Neurophysiol"},{"issue":"3","key":"260_CR8","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1097\/WNP.0b013e3180556926","volume":"24","author":"SP Fitzgibbon","year":"2007","unstructured":"Fitzgibbon SP, Powers DM, Pope KJ, Clark CR: Removal of EEG noise and artifact using blind source separation. J. Clin. Neurophysiol. 2007, 24(3):232-243. 10.1097\/WNP.0b013e3180556926","journal-title":"J. Clin. Neurophysiol"},{"issue":"4","key":"260_CR9","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1016\/j.neuroimage.2006.11.004","volume":"34","author":"A Delorme","year":"2007","unstructured":"Delorme A, Sejnowski T, Makeig S: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 2007, 34(4):1443-1449. 10.1016\/j.neuroimage.2006.11.004","journal-title":"NeuroImage"},{"key":"260_CR10","doi-asserted-by":"crossref","unstructured":"Halder S, Bensch M, Mellinger J, Bogdan M, Kubler A, Birbaumer N, Rosenstiel W: Online artifact removal for brain-computer interfaces using support vector machines and blind source separation. Comput. Intell. Neurosci. 2007, 10:. Article ID 82069","DOI":"10.1155\/2007\/82069"},{"issue":"3","key":"260_CR11","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s10439-008-9442-y","volume":"36","author":"M Crespo-Garcia","year":"2008","unstructured":"Crespo-Garcia M, Atienza M, Cantero JL: Muscle artifact removal from human sleep EEG by using independent component analysis. Ann. Biomed. Eng. 2008, 36(3):467-475. 10.1007\/s10439-008-9442-y","journal-title":"Ann. Biomed. Eng"},{"issue":"5","key":"260_CR12","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.clinph.2009.01.015","volume":"120","author":"FC Viola","year":"2009","unstructured":"Viola FC, Thorne J, Edmonds B, Schneider T, Eichele T, Debener S: Semi-automatic identification of independent components representing EEG artifact. Clin. Neurophysiol. 2009, 120(5):868-877. 10.1016\/j.clinph.2009.01.015","journal-title":"Clin. Neurophysiol"},{"issue":"3","key":"260_CR13","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1016\/j.neuroimage.2009.10.010","volume":"49","author":"BW McMenamin","year":"2010","unstructured":"McMenamin BW, Shackman AJ, Maxwell JS, Bachhuber DR, Koppenhaver AM, Greischar LL, Davidson RJ: Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG. NeuroImage 2010, 49(3):2416-2432. 10.1016\/j.neuroimage.2009.10.010","journal-title":"NeuroImage"},{"issue":"12 Pt 1","key":"260_CR14","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1109\/TBME.2006.879459","volume":"53","author":"W De Clercq","year":"2006","unstructured":"De Clercq W, Vergult A, Vanrumste B, Van Paesschen W, Van Huffel S: Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans. Biomed. Eng. 2006, 53(12 Pt 1):2583-2587.","journal-title":"IEEE Trans. Biomed. Eng"},{"issue":"5","key":"260_CR15","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1111\/j.1528-1167.2007.01031.x","volume":"48","author":"A Vergult","year":"2007","unstructured":"Vergult A, De Clercq W, Palmini A, Vanrumste B, Dupont P, Van Huffel S, Van Paesschen W: Improving the interpretation of ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia 2007, 48(5):950-958. 10.1111\/j.1528-1167.2007.01031.x","journal-title":"Epilepsia"},{"issue":"1","key":"260_CR16","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1177\/155005941004100111","volume":"41","author":"J Gao","year":"2010","unstructured":"Gao J, Zheng C, Wang P: Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis. Clin. EEG Neurosci. 2010, 41(1):53-59. 10.1177\/155005941004100111","journal-title":"Clin. EEG Neurosci"},{"issue":"7","key":"260_CR17","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.compbiomed.2008.04.010","volume":"38","author":"KP Indiradevi","year":"2008","unstructured":"Indiradevi KP, Elias E, Sathidevi PS, Dinesh Nayak S, Radhakrishnan K: A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Comput. Biol. Med. 2008, 38(7):805-816. 10.1016\/j.compbiomed.2008.04.010","journal-title":"Comput. Biol. Med"},{"issue":"3","key":"260_CR18","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.clinph.2006.10.024","volume":"118","author":"D Iyer","year":"2007","unstructured":"Iyer D, Zouridakis G: Single-trial evoked potential estimation: comparison between independent component analysis and wavelet denoising. Clin. Neurophysiol. 2007, 118(3):495-504. 10.1016\/j.clinph.2006.10.024","journal-title":"Clin. Neurophysiol"},{"issue":"9","key":"260_CR19","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1016\/j.csda.2004.12.010","volume":"50","author":"M Aminghafari","year":"2006","unstructured":"Aminghafari M, Cheze N, Poggi J-M: Multivariate denoising using wavelets and principal component analysis. Comput. Stat. Data Anal. 2006, 50(9):2381-2398. 10.1016\/j.csda.2004.12.010","journal-title":"Comput. Stat. Data Anal"},{"key":"260_CR20","first-page":"295","volume-title":"Wavelet-based EEG denoising for automatic sleep stage classification, in 21st International Conference on Electrical Communications and Computers (CONIELECOMP)","author":"E Estrada","year":"2011","unstructured":"Estrada E, Nazeran H, Sierra G, Ebrahimi F, Setarehdan SK: Wavelet-based EEG denoising for automatic sleep stage classification, in 21st International Conference on Electrical Communications and Computers (CONIELECOMP). , San Andres Cholula; 2011:295-298."},{"issue":"4","key":"260_CR21","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1088\/1741-2560\/3\/4\/011","volume":"3","author":"V Krishnaveni","year":"2006","unstructured":"Krishnaveni V, Jayaraman S, Anitha L, Ramadoss K: Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J. Neural Eng. 2006, 3(4):338-346. 10.1088\/1741-2560\/3\/4\/011","journal-title":"J. Neural Eng"},{"issue":"3","key":"260_CR22","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/LSP.2009.2037773","volume":"17","author":"J Gao","year":"2010","unstructured":"Gao J, Sultan H, Hu J, Tung WW: Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison. IEEE Signal Process. Lett. 2010, 17(3):237-240.","journal-title":"IEEE Signal Process. Lett"},{"issue":"9","key":"260_CR23","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/TBME.2010.2051440","volume":"57","author":"B Mijovic","year":"2010","unstructured":"Mijovic B, De Vos M, Gligorijevic I, Taelman J, Van Huffel S: Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Trans. Biomed. Eng. 2010, 57(9):2188-2196.","journal-title":"IEEE Trans. Biomed. Eng"},{"issue":"1","key":"260_CR24","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1088\/0031-9155\/32\/1\/004","volume":"32","author":"J Sarvas","year":"1987","unstructured":"Sarvas J: Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 1987, 32(1):11-22. 10.1088\/0031-9155\/32\/1\/004","journal-title":"Phys. Med. Biol"},{"key":"260_CR25","first-page":"206","volume-title":"Space or time adaptative signal processing by neural networks models, in Proceedings of the International Conference, vol. 151","author":"J Herault","year":"1986","unstructured":"Herault J, Jutten C: Space or time adaptative signal processing by neural networks models, in Proceedings of the International Conference, vol. 151. on Neural Networks for Computing, Snowbird; 1986:206-211."},{"key":"260_CR26","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","volume":"36","author":"P Comon","year":"1994","unstructured":"Comon P: Independent component analysis: a new concept? Signal Process. 1994, 36: 287-314. 10.1016\/0165-1684(94)90029-9","journal-title":"Signal Process"},{"issue":"1","key":"260_CR27","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/MSP.2008.4408442","volume":"25","author":"A Kachenoura","year":"2008","unstructured":"Kachenoura A, Albera L, Senhadji L, Comon P: ICA: a potential tool for BCI systems. Signal Process. Mag. IEEE 2008, 25(1):57-68.","journal-title":"Signal Process. Mag. IEEE"},{"key":"260_CR28","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1093\/biomet\/28.3-4.321","volume":"28","author":"H Hotelling","year":"1936","unstructured":"Hotelling H: Relations between two sets of variates. Biometrika 1936, 28: 321-377.","journal-title":"Biometrika"},{"issue":"2","key":"260_CR29","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1006\/nimg.2002.1067","volume":"16","author":"O Friman","year":"2002","unstructured":"Friman O, Borga M, Lundberg P, Knutsson H: Exploratory fMRI analysis by autocorrelation maximization. NeuroImage 2002, 16(2):454-464. 10.1006\/nimg.2002.1067","journal-title":"NeuroImage"},{"key":"260_CR30","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Z, Yen N-C, Tung CC, Liu HH: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 1998, 454: 903-995. 10.1098\/rspa.1998.0193","journal-title":"Proc. R. Soc. Lond. A"},{"issue":"1","key":"260_CR31","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/BF02344686","volume":"38","author":"H Liang","year":"2000","unstructured":"Liang H, Lin Z, McCallum RW: Artifact reduction in electrogastrogram based on empirical mode decomposition method. Med. Biol. Eng. Comput. 2000, 38(1):35-41. 10.1007\/BF02344686","journal-title":"Med. Biol. Eng. Comput"},{"key":"260_CR32","first-page":"489","volume-title":"Empirical mode decomposition, fractional Gaussian noise and Hurst exponent estimation, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 4","author":"G Rilling","year":"2005","unstructured":"Rilling G, Flandrin P, Goncalves P: Empirical mode decomposition, fractional Gaussian noise and Hurst exponent estimation, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 4. Pennsylvania Convention Center, Philadelphia; 2005:489-492."},{"issue":"3","key":"260_CR33","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jvoice.2005.12.009","volume":"20","author":"RM Roark","year":"2006","unstructured":"Roark RM: Frequency and voice: perspectives in the time domain. J. Voice 2006, 20(3):325-354. 10.1016\/j.jvoice.2005.12.009","journal-title":"J. Voice"},{"issue":"3","key":"260_CR34","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s00138-004-0170-5","volume":"16","author":"JC Nunes","year":"2005","unstructured":"Nunes JC, Guyot S, Delechelle E: Texture analysis based on local analysis of the bidimensional empirical mode decomposition. J. Mach. Vis. Appl. 2005, 16(3):177-188. 10.1007\/s00138-004-0170-5","journal-title":"J. Mach. Vis. Appl"},{"issue":"3","key":"260_CR35","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TSP.2010.2097254","volume":"59","author":"J Fleureau","year":"2011","unstructured":"Fleureau J, Nunes JC, Kachenoura A, Albera L, Senhadji L: Turning tangent empirical mode decomposition: a framework for mono- and multivariate signals. IEEE Trans. Signal Process. 2011, 59(3):1309-1316.","journal-title":"IEEE Trans. Signal Process"},{"issue":"5","key":"260_CR36","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/BF02584454","volume":"23","author":"L Senhadji","year":"1995","unstructured":"Senhadji L, Dillenseger JL, Wendling F, Rocha C, Kinie A: Wavelet analysis of EEG for three-dimensional mapping of epileptic events. Ann. Biomed. Eng. 1995, 23(5):543-552. 10.1007\/BF02584454","journal-title":"Ann. Biomed. Eng"},{"issue":"2","key":"260_CR37","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1109\/51.376755","volume":"14","author":"L Senhadji","year":"1995","unstructured":"Senhadji L, Carrault G, Bellanger JJ, Passariello G: Comparing wavelet transforms for recognizing cardiac patterns. IEEE Eng. Med. Biol. Mag. 1995, 14(2):167-73. 10.1109\/51.376755","journal-title":"IEEE Eng. Med. Biol. Mag"},{"issue":"3","key":"260_CR38","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/S0987-7053(02)00304-0","volume":"32","author":"L Senhadji","year":"2002","unstructured":"Senhadji L, Wendling F: Epileptic transient detection: wavelets and time-frequency approaches. Clin. Neurophysiol. 2002, 32(3):175-192. 10.1016\/S0987-7053(02)00304-0","journal-title":"Clin. Neurophysiol"},{"issue":"1","key":"260_CR39","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.jneumeth.2006.08.006","volume":"160","author":"X Li","year":"2007","unstructured":"Li X, Yao X, Fox J, Jefferys JG: Interaction dynamics of neuronal oscillations analysed using wavelet transforms. J. Neurosci. Methods 2007, 160(1):178-85. 10.1016\/j.jneumeth.2006.08.006","journal-title":"J. Neurosci. Methods"},{"issue":"4","key":"260_CR40","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1111\/j.1365-2869.1994.tb00135.x","volume":"3","author":"M Jobert","year":"1994","unstructured":"Jobert M, Tismer C, Poiseau E, Schulz H: Wavelets\u2014a new tool in sleep biosignal analysis. J. Sleep Res. 1994, 3(4):223-232. 10.1111\/j.1365-2869.1994.tb00135.x","journal-title":"J. Sleep Res"},{"issue":"12","key":"260_CR41","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/83.806616","volume":"8","author":"RW Buccigrossi","year":"1999","unstructured":"Buccigrossi RW, Simoncelli EP: Image compression via joint statistical characterization in the wavelet domain. IEEE Trans. Image Process. 1999, 8(12):1688-1701. 10.1109\/83.806616","journal-title":"IEEE Trans. Image Process"},{"issue":"6","key":"260_CR42","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1109\/JPROC.2009.2025663","volume":"98","author":"JL Starck","year":"2010","unstructured":"Starck JL, Bobin J: Astronomical data analysis and sparsity: from wavelets to compressed sensing. Proc. IEEE 2010, 98(6):1021-1030.","journal-title":"Proc. IEEE"},{"issue":"4","key":"260_CR43","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/18.86995","volume":"37","author":"S Mallat","year":"1991","unstructured":"Mallat S: Zero-crossings of a wavelet transform. IEEE Trans. Inf. Theory 1991, 37(4):1019-1033. 10.1109\/18.86995","journal-title":"IEEE Trans. Inf. Theory"},{"key":"260_CR44","doi-asserted-by":"publisher","first-page":"6063","DOI":"10.3390\/s100606063","volume":"10","author":"KM Chang","year":"2010","unstructured":"Chang KM: Arrhythmia ECG noise reduction by ensemble empirical mode decomposition. Sensors 2010, 10: 6063-6080. 10.3390\/s100606063","journal-title":"Sensors"},{"key":"260_CR45","doi-asserted-by":"crossref","unstructured":"Peng ZK, Tse PW, Chu FL: An improved Hilbert-Huang transform and its application in vibration signal analysis. J. Sound Vib. 2005,:187-205.","DOI":"10.1016\/j.jsv.2004.10.005"},{"key":"260_CR46","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1109\/TIM.2007.907967","volume":"56","author":"AO Boudraa","year":"2007","unstructured":"Boudraa AO, Cexus JC: EMD-based signal filtering. IEEE Trans. Instrum. Meas. 2007, 56: 2196-2202.","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"260_CR47","doi-asserted-by":"crossref","unstructured":"Kopsinis Y, McLaughlin S: Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans. Signal Process. 2009,:1351-1362.","DOI":"10.1109\/TSP.2009.2013885"},{"key":"260_CR48","first-page":"33","volume":"1","author":"AO Boudraa","year":"2004","unstructured":"Boudraa AO, Cexus JC, Saidi Z: EMD-based signal noise reduction. Int. J Signal Process. 2004, 1: 33-37.","journal-title":"Int. J Signal Process"},{"issue":"3","key":"260_CR49","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","volume":"41","author":"DL Donoho","year":"1995","unstructured":"Donoho DL: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 1995, 41(3):613-627. 10.1109\/18.382009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"260_CR50","volume-title":"Empirical mode decomposition based denoising techniques, in 1st International Work-shop on Cognitive Information Processing (CIP)","author":"Y Kopsinis","year":"2008","unstructured":"Kopsinis Y, McLaughlin S: Empirical mode decomposition based denoising techniques, in 1st International Work-shop on Cognitive Information Processing (CIP). , ; 2008."},{"issue":"432","key":"260_CR51","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1080\/01621459.1995.10476626","volume":"90","author":"DL Donoho","year":"1995","unstructured":"Donoho DL, Johnstone IM: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 1995, 90(432):1200-1224. 10.1080\/01621459.1995.10476626","journal-title":"J. Am. Stat. Assoc"},{"issue":"3","key":"260_CR52","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TBME.2006.890489","volume":"54","author":"D Cosandier-Rimele","year":"2007","unstructured":"Cosandier-Rimele D, Badier JM, Chauvel P, Wendling F: A physiologically plausible spatio-temporal model for EEG signals recorded with intracerebral electrodes in human partial epilepsy. IEEE Trans. Biomed. Eng. 2007, 54(3):380-388.","journal-title":"IEEE Trans. Biomed. Eng"},{"issue":"1","key":"260_CR53","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neuroimage.2008.04.185","volume":"42","author":"D Cosandier-Rimele","year":"2008","unstructured":"Cosandier-Rimele D, Merlet I, Badier JM, Chauvel P, Wendling F: The neuronal sources of EEG: modeling of simultaneous scalp and intracerebral recordings in epilepsy. NeuroImage 2008, 42(1):135-146. 10.1016\/j.neuroimage.2008.04.185","journal-title":"NeuroImage"},{"issue":"6","key":"260_CR54","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1097\/WNP.0b013e3182005dcd","volume":"27","author":"D Cosandier-Rimele","year":"2010","unstructured":"Cosandier-Rimele D, Merlet I, Bartolomei F, Badier JM, Wendling F: Computational modeling of epileptic activity: from cortical sources to EEG signals. J. Clin. Neurophysiol. 2010, 27(6):465-470. 10.1097\/WNP.0b013e3182005dcd","journal-title":"J. Clin. Neurophysiol"},{"issue":"4","key":"260_CR55","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s004220000160","volume":"83","author":"F Wendling","year":"2000","unstructured":"Wendling F, Bellanger JJ, Bartolomei F, Chauvel P: Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol. Cybern. 2000, 83(4):367-378. 10.1007\/s004220000160","journal-title":"Biol. Cybern"},{"issue":"6","key":"260_CR56","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1109\/TBME.2003.812164","volume":"50","author":"SI Goncalves","year":"2003","unstructured":"Goncalves SI, de Munck JC, Verbunt JP, Bijma F, Heethaar RM, Lopes da Silva F: In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head. IEEE Trans. Biomed. Eng. 2003, 50(6):754-767. 10.1109\/TBME.2003.812164","journal-title":"IEEE Trans. Biomed. Eng"},{"issue":"1","key":"260_CR57","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.neuroimage.2011.01.054","volume":"56","author":"G Birot","year":"2011","unstructured":"Birot G, Albera L, Wendling F, Merlet I: Localization of extended brain sources from EEG\/MEG: the ExSo-MUSIC approach. NeuroImage 2011, 56(1):102-113. 10.1016\/j.neuroimage.2011.01.054","journal-title":"NeuroImage"},{"issue":"7","key":"260_CR58","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1016\/j.clinph.2009.05.010","volume":"120","author":"H Hallez","year":"2009","unstructured":"Hallez H, De Vos M, Vanrumste B, Van Hese P, Assecondi S, Van Laere K, Dupont P, Van Paesschen W, Van Huffel S, Lemahieu I: Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging. Clin. Neurophysiol. 2009, 120(7):1262-1272. 10.1016\/j.clinph.2009.05.010","journal-title":"Clin. Neurophysiol"},{"issue":"1","key":"260_CR59","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.neuroimage.2007.07.025","volume":"38","author":"F Grouiller","year":"2007","unstructured":"Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahane P, David O: A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. NeuroImage 2007, 38(1):124-137. 10.1016\/j.neuroimage.2007.07.025","journal-title":"NeuroImage"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/1687-6180-2012-127.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/1687-6180-2012-127\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1687-6180-2012-127.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T19:37:18Z","timestamp":1630525038000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/1687-6180-2012-127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,7,2]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2012,12]]}},"alternative-id":["260"],"URL":"https:\/\/doi.org\/10.1186\/1687-6180-2012-127","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,7,2]]},"assertion":[{"value":"15 December 2011","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2012","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2012","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"127"}}