Information quantification of empirical mode decomposition and applications to field potentials

International Journal of Neural Systems

Volumn 21, Issue 1, 2011, Pages 49-63

  Mehboob, Zareen ^a   Yin, Hujun ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

empirical mode decomposition; information analysis; local field potentials; mutual information; Neuroinformatics; non stationary signal processing

Indexed keywords

EMPIRICAL MODE DECOMPOSITION; LOCAL FIELD POTENTIALS; MUTUAL INFORMATIONS; NEUROINFORMATICS; NONSTATIONARY SIGNAL PROCESSING;

INDEPENDENT COMPONENT ANALYSIS; SIGNAL PROCESSING; TIME SERIES;

INFORMATION ANALYSIS;

ALGORITHM; ANIMAL; ARTICLE; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPHY; METHODOLOGY; NERVE CELL NETWORK; PHYSIOLOGY; SIGNAL PROCESSING; VISUAL CORTEX;

ALGORITHMS; ANIMALS; BRAIN WAVES; ELECTROENCEPHALOGRAPHY; NERVE NET; SIGNAL PROCESSING, COMPUTER-ASSISTED; VISUAL CORTEX;

EID: 78751513653     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S012906571100264X     Document Type: Conference Paper

References (45)

1. M. J. Rasch, A. Gretton, Y. Murayama, W. Maass and N. K. Logothetis, Inferring spike trains from local field potentials, J. Neurophys. 99 (2008) 1461-1476.
2. G. Kreiman, C. P. Hung, A. Kraskov, R. Q. Quiroga, T. Poggio, J. J. DiCarlo, Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex, Neuron 49 (2006) 433-445.
3. S. Ray, S. S. Hsiao, N. E. Crone, P. J. Franaszczuk and E. Niebur, Effect of stimulus intensity on spike- LFP relationship in secondary somatosensory cortex, J. Neurosci. 28 (2008) 7334-7343.
4. A. Kraskov, R. Q. Quiroga, L. Reddy, I. Fried and C. Koch, Local field potentials and spikes in the human medial temporal lobe are selective to image category, J. Cognitive Neurosci. 19 (2007) 479-492.
5. P. Fries, A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence, Trends in Cognitive Sci. 9 (2005) 474-480.
6. M. Vejmelka, M. Palus and K. Susmakova, Identification of nonlinear oscillaotory activity embedded in broadband neural signals, Int. J. Neural Systems 20 (2010) 117-128.
7. A. D. Legatt, J. Arezzo and H. G. Vaughan, Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: Effects of volumeconducted potentials, J. Neurosci. Meth. 2 (1980) 203-217.
8. D. Rubino, K. A. Robbins and N. G. Hatsopoulos, Propagating waves mediate information transfer in the motor cortex, Nature Neurosci. 9 (2006) 1549-1557.
9. N. V. Manyakov and M. M. Van Hulle, Discriminating visual stimuli from local field potentials recorded with a multi-electrode array in the monkey's visual cortex, in IEEE Workshop on MLSP (2008) 157-162.
10. K. MacLeod, A. Bcker and G. Laurent, Who reads temporal information contained across synchronized and oscillatory spike trains? Nature 395 (1998) 693-698.
11. C. M. Sweeney-Reed and S. J. Nasuto, A novel approach to the detection of synchronization in EEG based on empirical mode decomposition, J. Comput. Neurosci. 23 (2007) 79-111.
12. M. A. Montemurro, M. J. Rasch, Y. Murayama, N. K. Logothetis and S. Panzeri, Phase-of-firing coding of natural visual stimuli in primary visual cortex, Current Bio. 18 (2008) 375-380.
13. J. D. Victor, Approaches to information-theoretic analysis of neural activity, Biol. Theory 1 (2006) 302-316.
14. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C.C. Tung and H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. The Royal Society 454 (1998) 903-995.
15. J. A. Henrie and R. Shapley, LFP power spectra in V1 cortex: The graded effect of stimulus contrast, J. Neurophys. 94 (2005) 479-490.
16. P. Berens, G. A. Keliris, A. S. Ecker, N. K. Logothetis and A. S. Tolias, Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex, Frontiers in Systems Neurosci. 2 (2008) 199-207.
17. H. Liang, S. L. Bressler, E. A. Buffalo, R. Desimone and P. Fries, Empirical mode decomposition of local field potentials from macaque V4 in visual spatial attention, Bio. Cyber. 92 (2005) 380-392.
18. A. Belitski, A. Gretton, C. Magri, Y. Murayama, M. A. Montemurro, N. K. Logothetis and S. Panzeri, Low frequency local field potentials and spikes in primary visual cortex convey independent visual information, J. Neurosci. 28 (2008) 5696-5709.
19. D. J. Krusienski, D. J. McFarland and J. R.Wolpaw, An evaluation of autoregressive spectral estimation model order for brain-computer interface applications, in Proc. 28th IEEE EMBS Annual Int. Conf. (2006) 1323-1326.
20. O. Faust, U. R. Acharya, L. C. Min and Bernhard H. C. Sputh, Automatic identification of epileptic and background EEG signals using frequency domain parameters, Int. J. Neural Systems 20 (2010) 159-176.
21. M. E. Davies and C. J. James, Source separation using single channel ICA, Signal Process. 87 (2007) 1819-1832.
22. P. S. Addison, Wavelet transforms and the ECG: A review, Physiol. Measurement 26 (2005) 155-199.
23. M. LeVan Quyen, J. Foucher, J. P. Lachaux, E. Rodriguez, A. Lutz, J. Martinerie and F.J. Varela, Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony, J. Neurosci. Meth. 111 (2001) 83-98.
24. Z. Mehboob and H. Yin, Analysis of non-stationary neurobiological signals using empirical mode decomposition, in Hybrid Artificial Intelligence Systems, HAIS 2008 (2008) 714-721.
25. H. Adeli, Z. Zhou and N. Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform, J. Neurosci. Meth. 123 (2003) 69-87.
26. P. Flandrin, G. Rilling and P. Goncalves, Empirical mode decomposition as a filter bank, in IEEE Sig. Proc. Lett. (2004).
27. G. Rilling, P. Flandrin and P. Gonalves, On empirical mode decomposition and its algorithms, in IEEEEURASIP Workshop on NSIP, url: http://perso. ens-lyon.fr/patrick.flandrin/NSIP03.pdf (2003).
28. Z. Wang, A. Maier, N. K. Logothetis and H. Liang, Single-trial classification of bistable perception by integrating empirical mode decomposition, clustering, and support vector machine, EURASIP J. on Advances in Signal Process. (2008) 1-8.
29. B. Mijovic, M. De Vos, I. Gligorijevic, J. Taelman and S. Van Huffel, Source separation from singlechannel recordings by combining empirical mode decomposition and independent component analysis, IEEE Trans. Bio. Eng. 57 (2010) 1-9.
30. M. Lambert, A. Engroff, M. Dyer and B. Byer, EMD, url: http://www.owlnet.rice.edu/elec301/Proj ects02/empirical Mode.
31. D. Kim and H. S. Oh, EMD: Empirical mode decomposition and Hilbert spectral analysis, url: http:// cran.r-project.org/web/packages/EMD/index.html (2008).
32. M. Ortigueira, Empirical mode decomposition, url: http://www.mathworks. com/matlabcentral/fileexchange/ 21409-empirical-mode-decomposition (2008).
33. Z. Mehboob and H. Yin, Information preserving empirical mode decomposition for filtering field potentials, in Int. Conf. IDEAL (2009) 226-233.
34. T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley Interscience (1999).
35. L. F. Abbot and P. Dayan, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems MIT Press (2001).
36. E. Arabzadeh, S. Panzeri and M. E. Diamond, Whisker vibration information carried by rat barrel cortex neurons, J. Neurosci. 24 (2004) 6011-6020.
37. A. Oliva, Gist of the scene, in Neurobiology of Attention L. Itti and G.Rees and J.K. Tsotsos (eds.) San Diego, CA: Elsevier (2005) 251-256.
38. D. B. Percival and A. T. Walden, Spectral analysis for physical applications: Multitaper and conventional univariate techniques, Cambridge Uni. Press (1993).
39. X. Wang, Y. Chen, S. Bressler and M. Ding, Evaluating causal relations among multiple neurobiological time series: Blockwise versus pairwise Granger causality, Int. J. Neural Systems 17(2) (2007) 71-78.
40. N. V. Manyakov and M. M. Van Hulle, Synchronization in monkey visual cortex analyzed with an information-theoretic measure, Chaos: An Interdisciplinary J. Nonlinear Sci. 18 (2008) 1-7.
41. E. Franko, A. R. Seiz and R. Vogels, Dissociable neural effects of long term stimulus-reward pairing in macaque visual cortex, J. of Cognitive Neurosc. 22 (2010) 1425-1439.
42. Z. Mehboob, Information preserving EMD based toolbox, url: http://personalpages.manchester.ac. uk/postgrad/Zareen.Mehboob (2010).
43. M. A. Kramer, F. L. Chang, M. E Cohen, D. Hudson and A.J. Szeri, Synchronization measures of the scalp EEG can discriminate healthy from Alzheimers subjects, Int. J. Neural Systems 17 (2007) 61-69.
44. M. Ahmadlou and H. Adeli,Wavelet synchronization methodology: a new approach for EEG-based diagnosis of ADHD, Clinical EEG Neurosci. 41 (2010) 1-10.
45. C. M. Sweeney-Reed and S. J. Nasuto, Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis, J. Neurosci. Meth. 184 (2009) 54-70.