Decomposition of synthetic multi-channel surface-electromyogram using independent component analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3195, Issue , 2004, Pages 985-992

  García, Gonzalo A ^a   Maekawa, Kazuya ^a   Akazawa, Kenzo ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; ELECTROPHYSIOLOGY;

ELECTRODE ARRAYS; ICA ALGORITHMS; INDEPENDENT COMPONENT ANALYSIS(ICA); MOTOR UNIT ACTION POTENTIALS; MULTI CHANNEL; SIGNAL PREPROCESSING; SYNTHETIC SIGNALS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 35048877131     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30110-3_124     Document Type: Article

References (35)

1. th edn., Williams & Wilkins, Baltimore (1985)
2. E. Henneman, G. Somjen, and D.O. Carpenter: Functional significance of cell size in spinal motoneurons. J. Neurophysiol., vol.28, pp. 560-580 (1965)
3. A.M. Halliday, S.R. Butler, and R. Paul (Eds): A Textbook of Clinical Neurophysiology, John Wiley & Sons, New York (1987)
4. R. Merletti, D. Farina, and A. Granata: Non-Invasive Assessment of Motor Unit Properties with Linear Electrode Arrays, In: Clinical Neurophysiology: From Receptors to Perception (EEG Suppl. 50). Editors: G. Comi, C.H. Lücking, J. Kimura, and P.M. Rossini, pp. 293-300 (1999)
5. B. Freriks and H.J. Hermens: Roessingh Research and Development b.v., European Recommendations for Surface ElectroMyoGraphy, results of the SENIAM project (1999), ISBN: 90-75452-14-4 (CD-ROM)
6. D. Stashuk: EMG signal decomposition: how can it be accomplished and used?, J. Electromyogr. Kines., vol. 11, pp. 151-173 (2001)
7. E.J. De la Barrera and T.E. Milner: The effect of skinfold thickness on the selectivity of surface EMG, Electroencephalography and Clinical Neurophysiology, vol. 93, pp. 91-99 (1993)
8. D. Farina, C. Cescon, and R. Merletti: Influence of anatomical, physical, and detection-system parameters on surface EMG, Biol. Cybern., vol. 86, pp. 445-456 (2002)
9. E. Huigen, A. Peper, and C. A. Grimbergen: Investigation into the origin of the noise of surface electrodes, Med. Biol. Eng. Comput., vol. 40, pp. 332-338 (2002)
10. Z. Xu, S. Xiao, and Z. Chi: ART2 neural network for surface EMG decomposition, Neural Computing & Applications, vol. 10, pp. 29-38 (2001)
11. T.-Y. Sun, T.-S. Lin, and J.-J. Chen: Multielectrode surface EMG for noninvasive estimation of motor unit size, Muscle & Nerve, vol. 22, pp. 1063-1070 (1999)
12. rd Ann. Intl. Conf. of the IEEE EMBS, Istanbul; pp. 1066-1069 (2001)
13. D. Stashuk and Y. Qu: Robust method for estimating motor unit firing-pattern statistics, Medical & Biological Engineering & Computing, vol. 34, pp. 50-57 (1996).
14. S. Karlsson, J. Yu, and M. Akay: Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study, IEEE Transactions on Biomedical Engineering, vol. 47 (2), pp. 228-238 (2000)
15. st International IEEE EMBS Conference on Neural Engineering, Capri Island, Italy, pp. 372-375 (2003)
16. J.-Y. Hogrel: Use of surface EMG for studying motor unit recruitment during isometric linear force ramp, Journal of Electromyography and Kinesiology, vol. 13(5), pp. 417-23 (2003)
17. G.A. García, R. Okuno, and K. Akazawa: Decomposition Algorithm for Surface Electrode-Array Electromyogram in Voluntary Isometric Contraction, IEEE BME Magazine, vol. 23(5) (2004) [Accepted]
18. P. Comon: Independent component analysis, a new concept?, Signal Processing, vol. 36, pp. 287-314 (1994)
19. J.-F. Cardoso: Blind signal separation: statistical principles, Proceedings of the IEEE, vol. 86(10), pp. 2009-2025 (1998)
20. Hyvärinen and E. Oja: Independent component analysis: algorithms and applications, Neural Networks, vol. 13, pp. 411-430 (2000)
21. B. Mambrito and C.J. De Luca: A technique for the detection, decomposition and analysis of the EMG signal, Electroencephalogra. Clin. Neurophysiol., vol.58, pp.175-188 (1984)
22. C. Disselhorst-Kulg, J. Silny, and G. Rau: Estimation of the Relationship Between the Noninvasively Detected Activity of Single Motor Units and Their Characteristic Pathological Changes by Modelling, J. of Electromyography and Kinesiology, vol. 8, pp. 323-335 (1998)
23. S. Andreassen and A. Rosenfalck: Relationship of Intracellular and Extracellular Action Potentials of Skeletal Muscle Fibers, CRC Crit. Rev. Bioeng., vol. 6(4), pp. 267-306 (1981)
24. H.P. Clamann: Statistical Analysis of Motor Unit Firing Patterns in a Human Skeletal Muscle, Biophysical Journal, vol. 9, pp. 1233-1251 (1969)
25. J.F. Cardoso and A. Souloumiac: Blind Beamforming for Non-Gaussian Signals, IEE Proceedings-F, vol. 140(6) (1993)
26. A. Hyvärinen and E. Oja: A Fast Fixed-Point Algorithm for Independent Component Analysis, Neural Computation, vol. 9, MIT Press, Cambridge, pp. 1483-1492 (1997)
27. D. Xu, J.C. Principe, J. Fisher III, and H.-C. Wu: A Novel Measure for Independent Component Analysis (ICA), ICASSP '98, Seattle, vol.2, pp. 1161-1164 (1998)
28. D. Tjøstheim: Measures of Dependence and Tests of Independence, Statistics, vol. 28, pp. 249-282 (1996)
29. S. Amari, A. Cichocki, and H.H. Yang: A New Learning Algorithm for Blind Signal Separation, Advances in Neural Information Processing Systems, vol. 8, Touretzky, Mozer and Hasselmo Ed., MIT Press (1996)
30. rd ed., Ediciones Sol, Madrid (1987), pp. 541-543
31. E. Parzen: On Estimation of a Probability Density Function and Mode, Annals of Mathematical Statistics, vol. 33, pp. 1065-1076 (1962)
32. M.P. Wand and M.C. Jones: Kernel Smoothing, Chapman & Hall, London (1995)
33. D. Erdogmus, L. Vielva, and J.C. Principe: Nonparametric Estimation and Tracking of the Mixing Matrix for Underdetermined Blind Source Separation, Proc. of ICA'01, San Diego, California, pp. 189-194 (2001)
34. nd ed., Wiley, New York (2001)
35. F.R. Bach and M.I. Jordan: Kernel Independent Component Analysis, Journal of Machine Learning Research, vol. 3 (2002), pp. 1-48