Time-shift denoising source separation

Journal of Neuroscience Methods

Volumn 189, Issue 1, 2010, Pages 113-120

  de Cheveigné, Alain ^a,b  

^a UNIVERSITÉ PARIS DESCARTES   (France)

^b ECOLE NORMALE SUPÉRIEURE   (France)

Author keywords

Artifact removal; EEG; Electroencephalography; Magnetoencephalography; MEG; Noise reduction; Principal component analysis

Indexed keywords

ALGORITHM; ANIMAL EXPERIMENT; ARTICLE; ARTIFACT REDUCTION; CONTROLLED STUDY; ELECTROENCEPHALOGRAPHY; FILTER; GUINEA PIG; HUMAN; HUMAN EXPERIMENT; MAGNETOENCEPHALOGRAPHY; MULTICHANNEL RECORDER; NEUROPHYSIOLOGY; NOISE REDUCTION; NONHUMAN; NORMAL HUMAN; PRIORITY JOURNAL; REPRODUCIBILITY; SIGNAL NOISE RATIO; SIGNAL PROCESSING; SIMULATION;

ALGORITHMS; ANIMALS; ARTIFACTS; BRAIN; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; EVOKED POTENTIALS; GUINEA PIGS; HUMANS; MAGNETOENCEPHALOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS;

EID: 77952549108     PISSN: 01650270     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2010.03.002     Document Type: Article

References (32)

1. Adachi Y., Kawai J., Miyamoto M., Ogata H., Tomori M., Kawabata S., et al. A SQUID system for measurement of spinal cord evoked field of supine subjects. IEEE Trans Applied Supercond 2009, 19:861-866.
2. Ahonen A.I., Hämäläinen M.S., Ilmoniemi R.J., Kajola M.J., Knuutila E.T., Simola J.T., et al. Sampling theory for neuromagnetic detector arrays. IEEE Trans Biomed Eng 1993, 40:859-868.
3. Anemuller J., Sejnowski T.J., Makeig S. Complex spectral-domain independent component analysis of electroencephalographic data. 4th international symposium on independent component analysis and blind signal separation 2003, p. 47-52.
4. Beucker R, Schlitt HA. Temporal and spatial prewhitening of multichannel MEG data. Jülich Supercomputing Center technical report FZJ-ZAM-IB-9721; 1997.
5. Boudet S., Peyrodie L., Gallois P., Vasseur C. Filtering by optimal projection and application to automatic artifact removal from EEG. Signal Proc 2007, 87:1978-1992.
6. Blankertz B., Tomioka R., Lemm S., Kawanabe M., Müller K-R. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 2008, 25:41-56.
7. Cichocki A. Blind signal processing methods for analyzing multichannel brain signals. Int J Bioelectromagn 2004, 6:1-18.
8. de Cheveigné A., Simon J.Z. Denoising based on time-shift PCA. J Neurosci Methods 2007, 165:297-305.
9. de Cheveigné A., Simon J.Z. Sensor noise suppression. J Neurosci Methods 2008, 168:195-202.
10. de Cheveigné A., Simon J.Z. Denoising based on spatial filtering. J Neurosci Methods 2008, 171:331-339.
11. Dornhege G., Blankertz B., Krauledat M., Losch F., Curio G., Müller K-R. Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomedical Eng 2006, 11:2274-2281.
12. Efron B., Tibshirani R.J. Introduction to the bootstrap. Monographs on statistics and applied probability 1993, Chapman and Hall/CRC.
13. Friman O., Borga M., Lundberg P., Knutsson H. Exploratory fMRI analysis by autocorrelation maximization. Neuroimage 2002, 16:454-464.
14. Fukunaga K. Introduction to statistical pattern recognition 1990, Academic Press. 2nd ed.
15. Gruber P., Stadlthanner K., Böhm M., Theis F.J., Lang E.W., Tomé A.M., et al. Denoising using local projective subspace methods. Neurocomputing 2006, 69:1485-1501.
16. Hotelling H. Relations between two sets of variants. Biometrika 1936, 28:321-377.
17. Kado H., Higuchi M., Shimogawara M., Haruta Y., Adachi Y., Kawai J., et al. Magnetoencephalogram systems developed at KIT. IEEE Trans Appl Supercond 1999, 9:4057-4062.
18. Koles Z.J., Lazar M.S., Zhou S.Z. Spatial patterns underlying population differences in the background EEG. Brain Topogr 1990, 2:275-284.
19. Miyamoto M., Kawai J., Kawabata M., Shimozu T., Adachi Y., Uehara G., et al. Development of a biomagnetism measurement system for small animals. Int Congr Series 2007, 1300:586-590.
20. Miyamoto M., Kawai J., Adachi Y., Haruta Y., Komamura K., Uehara G Development of an MCG/MEG system for small animals and its noise reduction method. J Phys: Conf Series 2008, 97:012258.
21. Parra L.C., Spence C.D., Gerson A.D., Sajda P. Recipes for the linear analysis of EEG. Neuroimage 2005, 28:326-341.
22. Sander T.H., Wubbeler G., Lueschow A., Curio G., Trahms L. Cardiac artifact subspace identification and elimination in cognitive MEG data using time-delayed decorrelation. IEEE Trans Biomed Eng 2002, 49:345-354.
23. Särelä J. Exploratory source separation in biomedical systems. Technical University of Helsinki, unpublished thesis; 2004.
24. Särelä J., Valpola H. Denoising source separation. J Mach Learn Res 2005, 6:233-272.
25. Sekihara K., Nagarajan S., Poeppel D., Miyashita Y. Reconstructing spatio-temporal activities of neural sources from magnetoencephalographic data using a vector beamformer. IEEE ICASSP 2001, 3:2021-2024.
26. Sekihara K., Hild K.E., Nagarajan S.S. A novel adaptive beamformer for MEG source reconstruction effective when large background brain activities exist. IEEE Trans Biomed Eng 2006, 53:1755-1764.
27. Taulu S., Simola J., Kajola M. Applications of the signal space separation method. IEEE Trans Biomed Eng 2005, 53:3359-3372.
28. Valpola H, Pajunen P. Fast algorithms for Bayesian independent component analysis. In: Proceedings of the second international workshop on independent component analysis and blind signal separation (ICA 2000); 2000: p. 233-7.
29. Vrba J, Robinson SE, McCubbin J. How many channels are needed for MEG? Neurology, Neurophysiology and Neuroscience 2004; 99:1-5, Available at: Date accessed: 12 November 2009. http://www.neurojournal.com/article/view/313.
30. Woon W.L., Lowe D. Can we learn anything from single-channel unaveraged MEG data?. Neural Comput Appl 2004, 13:360-368.
31. Xie F, Le Cerf P, Van Compernolle D. Linear prediction analysis of multichannel speech recordings. In: Proc. ProRisc IEEE Benelux workshop on circuits, systems and signal processing, 1992; 277-82.
32. Ziehe A., Müller K-R., Nolte G., Mackert B.M., Curio G. Artifact reduction in magnetoneurography based on time-delayed second-order correlations. IEEE Trans Biomed Eng 2000, 47:75-87.