Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

NeuroImage

Volumn 49, Issue 1, 2010, Pages 257-271

  Hyvärinen, Aapo ^a   Ramkumar, Pavan ^b   Parkkonen, Lauri ^b   Hari, Riitta ^b  

^a HELSINKI INSTITUTE FOR INFORMATION TECHNOLOGY   (Finland)

^b AALTO UNIVERSITY   (Finland)

Author keywords

Brain rhythms; Independent component analysis; Magnetoencephalography (MEG); Resting state

Indexed keywords

ARTICLE; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; FOURIER TRANSFORMATION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; INDEPENDENT COMPONENT ANALYSIS; MAGNETOENCEPHALOGRAPHY; PRIORITY JOURNAL; REST; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; SIMULATION;

ALGORITHMS; BRAIN; ELECTROENCEPHALOGRAPHY; FOURIER ANALYSIS; HUMANS; MAGNETOENCEPHALOGRAPHY; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 70350070225     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.08.028     Document Type: Article

References (32)

1. Amari S.-I., Cichocki A., and Yang H. A new learning algorithm for blind source separation. In: Touretzky D.S., Mozer M.C., and Hasselmo M.E. (Eds). Advances in Neural Information Processing Systems 8 (1996), MIT Press, Cambridge, MA 757-763
2. Anemüller J., Sejnowski T.J., and Makeig S. Complex independent component analysis of frequency-domain electroencephalographic data. Neural Netw. 16 (2003) 1311-1323
3. Anemüller J., Duann J.-R., Sejnowski T.J., and Makeig S. Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis. Neurocomputing 69 (2006) 1502-1512
4. Bartels A., and Zeki S. The chronoarchitecture of the human brain - natural viewing conditions reveal a time-based anatomy of the brain. NeuroImage 22 (2004) 419-433
5. Beale E.M.L., and Mallows C.L. Scale mixing of symmetric distributions with zero means. Ann. Math. Stat. 30 4 (1959) 1145-1151
6. Beckmann C.F., DeLuca M., Devlin J.T., and Smith S.M. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360 1457 (2005) 1001-1013
7. Bell A.J., and Sejnowski T.J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7 (1995) 1129-1159
8. Belouchrani A., Meraim K.A., Cardoso J.-F., and Moulines E. A blind source separation technique based on second order statistics. IEEE Trans. Signal Process. 45 2 (1997) 434-444
9. Bingham E., and Hyvärinen A. A fast fixed-point algorithm for independent component analysis of complex-valued signals. Int. J. Neural Syst. 10 1 (2000) 1-8
10. Cardoso J.-F., and Souloumiac A. Blind beamforming for non Gaussian signals. IEE Proceedings-F 140 6 (1993) 362-370
11. Comon P. Independent component analysis-a new concept?. Signal Process. 36 (1994) 287-314
12. Delfosse N., and Loubaton P. Adaptive blind separation of independent sources: a deflation approach. Signal Process. 45 (1995) 59-83
13. Dyrholm M., Makeig S., and Hansen L.K. Model selection for convolutive ICA with an application to spatio-temporal analysis of EEG. Neural Comput. 19 4 (2007) 934-955
14. Groppe, D.M., Makeig, S., Kutas, M., 2009. Identifying reliable independent components via split-half comparisons. NeuroImage. 45 (4), 1199-1211.
15. Hasson U., Nir Y., Levy I., Fuhrmann G., and Malach R. Intersubject synchronization of cortical activity during natural vision. Science 303 (2004) 1634-1640
16. Himberg J., Hyvärinen A., and Esposito F. Validating the independent components of neuroimaging time-series via clustering and visualization. NeuroImage 22 3 (2004) 1214-1222
17. Horn R.A., and Johnson C.R. Matrix Analysis (1985), Cambridge University Press
18. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10 3 (1999) 626-634
19. Hyvärinen A., and Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput. 9 7 (1997) 1483-1492
20. Hyvärinen A., and Hurri J. Blind separation of sources that have spatiotemporal variance dependencies. Signal Process. 84 2 (2004) 247-254
21. Hyvärinen A., Karhunen J., and Oja E. Independent Component Analysis (2001), Wiley Interscience
22. Jung T.P., Makeig S., Humphries C., Lee T.-W., McKeown M.J., Iragui V., and Sejnowski T. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37 (2000) 163-178
23. Kiviniemi V., Kantola J.-H., Jauhiainen J., Hyvärinen A., and Tervonen O. Independent component analysis of nondeterministic fMRI signal sources. NeuroImage 19 2 (2003) 253-260
24. Miwakeichi F., Martnez-Montes E., Valdés-Sosa P.A., Nishiyama N., Mizuhara H., and Yamaguchi Y. Decomposing EEG data into space-time-frequency components using parallel factor analysis. NeuroImage 22 3 (2004) 1035-1045
25. Raichle M.E., MacLeod A.M., Snyder, A.Z., Powers W.J., Gusnard D.A., and Shulman G.L. A default mode of brain function. Proc. Nat. Acad. Sci. U. S. A. 98 (2001) 676-682
26. Ramkumar P., Parkkonen L.T., He B.J., Raichle M.E., Hämäläinen M.S., and Hari R. Identification of stimulus-related and intrinsic networks by spatial independent component analysis of MEG signals. Abstract presented at the Society for Neuroscience Meeting (2007), San Diego, California
27. Sarvas J. Basic mathematical and electromagnetic concepts of the bio-magnetic inverse problems. Phys. Med. Biol. 32 (1987) 11-22
28. Taulu S., Kajola M., and Simola J. Suppression of interference and artifacts by the signal space separation method. Brain Topogr. 16 (2004) 269-275
29. van de Ven V.G., Formisano E., Prvulovic D., Roeder C.H., and Linden D.E. Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Hum. Brain Mapp. 22 3 (2004) 165-178
30. Vigário R., Särelä J., Jousmäki V., Hämäläinen M., and Oja E. Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47 5 (2000) 589-593
31. Zibulevsky M., and Pearlmutter B.A. Blind source separation by sparse decomposition in a signal dictionary. Neural Comput. 13 4 (2001) 863-882
32. Ziehe A., and Müller K.-R. TDSEP-an efficient algorithm for blind separation using time structure. Proc. Int. Conf. on Artificial Neural Networks (ICANN'98) (1998), Skövde, Sweden 675-680