Consistency of the blind source separation computed with five common algorithms for magnetoencephalogram background activity

Medical Engineering and Physics

Volumn 32, Issue 10, 2010, Pages 1137-1144

  Escudero, Javier ^a,b   Hornero, Roberto ^a   Abásolo, Daniel ^a,c  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

^c UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

Algorithm comparison; Blind Source Separation (BSS); Consistency; Independent Component Analysis (ICA); Magnetoencephalogram (MEG)

Indexed keywords

ALGORITHM COMPARISON; BRAIN DATA; BRAIN SIGNALS; CONSISTENCY; FACTOR ANALYSIS; FASTICA; INDEPENDENT COMPONENTS; INFOMAX; MAGNETOENCEPHALOGRAMS; NUMBER OF COMPONENTS; STATISTICAL CRITERION; SYNTHETIC SIGNALS; UNDERLYING COMPONENTS;

INDEPENDENT COMPONENT ANALYSIS; SIGNAL ANALYSIS; SILICATE MINERALS;

BLIND SOURCE SEPARATION;

ACCURACY; AGED; ALGORITHM; ALGORITHM FOR MULTIPLE UNKNOWN SIGNALS EXTRACTION; ARTICLE; BLIND SOURCE SEPARATION; BRAIN ELECTROPHYSIOLOGY; CONTROLLED STUDY; EXTENDED INFOMAX ALGORITHM; FACTORIAL ANALYSIS; FASTLCA ALGORITHM; FEMALE; HUMAN; HUMAN EXPERIMENT; JOINT APPROXIMATE DIAGONALISATION OF EIGENMATRICES; MAGNETOENCEPHALOGRAPHY; MALE; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; NORMAL HUMAN; PRIORITY JOURNAL; SECOND ORDER BLIND IDENTIFICATION; STANDARDIZATION;

AGED; ALGORITHMS; ARTIFACTS; BRAIN MAPPING; FEMALE; HUMANS; MAGNETOENCEPHALOGRAPHY; MALE; MIDDLE AGED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 78449248945     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2010.08.005     Document Type: Article

References (34)

1. Hari R. Magnetoencephalography in clinical neurophysiological assessment of human cortical functions. Electroencephalography: basic principles, clinical applications, and related fields 2004, 1165-1167. Lippincott Williams and Wilkins, Philadelphia, USA. E. Niedermeyer, F. Lopes da Silva (Eds.).
2. Onton J., Westerfield M., Townsend J., Makeig S. Imaging human EEG dynamics using independent component analysis. Neuroscience and Biobehavioral Reviews 2006, 30(6):808-822.
3. James C., Hesse C. Independent component analysis for biomedical signals. Physiological Measurement 2005, 26(1):R15-39.
4. Hyvärinen A., Karhunen J., Oja E. Independent component analysis 2001, John Wiley & Sons, NJ, USA.
5. Vigário R., Oja E. BSS and ICA in neuroinformatics: from current practices to open challenges. IEEE Reviews in Biomedical Engineering 2008, 1:50-61.
6. Zavala Fernández H., Sander T., Burghoff M., Orglmeister R., Trahms L. Comparison of ICA algorithms for the isolation of biological artifacts in magnetoencephalography. Lecture Notes in Computer Science 2006, 3889:511-518.
7. 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.
8. Fitzgibbon S., Powers D., Pope K., Clark C. Removal of EEG noise and artifact using blind source separation. Journal of Clinical Neurophysiology 2007, 24(3):232-243.
9. Escudero J., Hornero R., Abásolo D., Fernández A., Lopez-Coronado M. Artifact removal in magnetoencephalogram background activity with independent component analysis. IEEE Transactions on Biomedical Engineering 2007, 54(11):1965-1973.
10. Klemm M., Haueisen J., Ivanova G. Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity. Medical & Biological Engineering & Computing 2009, 47(4):413-423.
11. Melissant C., Ypma A., Frietman E., Stam C. A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements. Artificial Intelligence in Medicine 2005, 33(3):209-222.
12. Escudero J., Hornero R., Poza J., Abásolo D., Fernández A. Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation. Artificial Intelligence in Medicine 2008, 43(1):75-85.
13. Escudero J., Hornero R., Abásolo D., Fernández A. Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease. Medical Engineering and Physics 2009, 31(7):872-879.
14. Li Y., Powers D., Peach J. Comparison of blind source separation algorithms. Advances in Neural Networks and Applications 2001, 18-23.
15. Crespo-Garcia M., Atienza M., Cantero J. Muscle artifact removal from human sleep EEG by using independent component analysis. Annals of Biomedical Engineering 2008, 36(3):467-475.
16. Romero S., Mañanas M., Barbanoj M. A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case. Computers in Biology and Medicine 2008, 38(3):348-360.
17. Romero S., Mañanas M., Barbanoj M. Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation. Annals of Biomedical Engineering 2009, 37(1):176-191.
18. Krishnaveni V., Jayaraman S., Kumar P., Shivakumar K., Ramadoss K. Comparison of independent component analysis algorithms for removal of ocular artifacts from electroencephalogram. Measurement Science Review Journal 2005, 5(2):67-79.
19. Krishnaveni V., Jayaraman S., Ramadoss K. Application of mutual information based least dependent component analysis (MILCA) for removal of ocular artifacts from electroencephalogram. International Journal of Biological and Medical Sciences 2006, 1(1):63-74.
20. Hyvärinen A., Ramkumar P., Parkkonen L., Hari R. Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage 2010, 49(1):257-271.
21. Hesse C. Model order estimation for blind source separation of multichannel magnetoencephalogram and electroencephalogram signals. Proceedings of the 30th annual international conference of the IEEE Engineering in Medicine and Biology Society 2008, 3348-3351.
22. Hesse C. On estimating the signal subspace dimension of high-density multichannel magnetoencephalogram measurements. Proceedings of the 29th annual international conference of the IEEE Engineering in Medicine and Biology Society 2007, 6227-6230.
23. Tong L., Liu R., Soon V., Huang Y. Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems 1991, 38(5):499-509.
24. Belouchrani A., Abed-Meraim K., Cardoso J., Moulines E. A blind source separation technique using second-order statistics. IEEE Transactions on Signal Processing 1997, 45(2):434-444.
25. Cardoso J., Souloumiac A. Blind beamforming for non gaussian signals. IEE Proceedings. Part F. Radar and Signal Processing 1993, 140(6):362-370.
26. Lee T., Girolami M., Sejnowski T. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation 1999, 11(2):417-441.
27. Delorme A., Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 2004, 134(1):9-21.
28. Gävert H, Hurri J, Särelä J, Hyvärinen A. FastICA Toolbox, website [online] http://www.cis.hut.fi/projects/ica/fastica/ [March 2010].
29. Cichocki A, Amari S, Siwek K, Tanaka T, Huy Phan A. ICALAB for signal processing, website [online] http://www.bsp.brain.riken.jp/ICALAB/ICALABSignalProc/ [March 2010].
30. Ikeda S., Toyama K. Independent component analysis for noisy data-MEG data analysis. Neural Networks 2000, 13(10):1063-1074.
31. Cao J., Murata N., Amari S., Cichocki A., Takeda T. A robust approach to independent component analysis of signals with high-level noise measurements. IEEE Transactions on Neural Networks 2003, 14(3):631-645.
32. Mansour A., Kawamoto M., Ohnishi N. A survey of the performance indexes of ICA algorithms. Proceedings of the 21st IASTED international conference: modelling, identification and control (MIC'02) 2002, 660-666.
33. Vincent E., Gribonval R., Févotte C. Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech and Language Processing 2006, 14(4):1462-1469.
34. McNames J. An effective color scale for simultaneous color and gray-scale publications. IEEE Signal Processing Magazine 2006, 23:82-96.