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Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review


The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

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  1. 1.

    ICA based identification of sources in sEMG (2007) doi:10.1109/ISSNIP.2007.4496914

  2. 2.

    Acharya Bhat SP, Kannathal N, Rao A, Lim CM (2005) Analysis of cardiac health using fractal dimension and wavelet transformation. ITBM-RBM 26(2):133–139

    Article  Google Scholar 

  3. 3.

    Akujuobi C, Baraniecki A (1992) Wavelets and fractals: a comparative study. Statistical signal and array processing, 1992. Conference proceedings., IEEE sixth SP workshop on pp. 42–45

  4. 4.

    Anmuth CJ, Goldberg G, Mayer NH (1994) Fractal dimension of electromyographic signals recorded with surface electrodes during isometric contractions is linearly correlated with muscle activation. Muscle Nerve 17(8):953–954

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Arjunan SP, Kumar DK (2007) Fractal based modelling and analysis of electromyography (EMG) to identify subtle actions. 29th Annual international conference of the IEEE engineering in medicine and biology society pp. 1961–1964

  6. 6.

    Attias H, Schreiner CE (1998) Blind source separation and deconvolution: the dynamic component analysis algorithm. Neural Comput 10(6):1373–1424

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Azzerboni B, Carpentieri M, La Foresta F, Morabito FC (2004) Neural-ICA and wavelet transform for artifacts removal in surface EMG. In: Neural networks, 2004. Proceedings. 2004 IEEE international joint conference on 4:3223–3228

  8. 8.

    Azzerboni B, Finocchio G, Ipsale M, La Foresta F, Mckeown MJ, Morabito FC (2002) Spatio-temporal analysis of surface electromyography signals by independent component and time-scale analysis. In: Engineering in medicine and biology, 2002. 24th Annual conference and the annual fall meeting of the biomedical engineering society. EMBS/BMES conference, 2002. Proceedings of the 2nd joint 1:112–113. doi:10.1109/IEMBS.2002.1134411

  9. 9.

    Baris N (2007) The adaptive ARMA analysis of EMG signals. J Med Sys 32(1):43–50

    Article  Google Scholar 

  10. 10.

    Barlow JS (1979) Computerized clinical electroencephalography in perspective. IEEE Trans Biomed Eng BME-26(7):377–391. doi:10.1109/TBME.1979.326416

    Article  Google Scholar 

  11. 11.

    Bartolo A, Roberts C, Dzwonczyk RR, Goldman E (1996) Analysis of diaphragm EMG signals: comparison of gating vs. subtraction for removal of ecg contamination. J Appl Physiol 80(6):1898–1902

    PubMed  CAS  Google Scholar 

  12. 12.

    Basmajian Deluca C (1985) Muscles alive: their functions revealed by electromyography, 5th edn. Williams & Wilkins, Baltimore, USA

    Google Scholar 

  13. 13.

    Bassingthwaighte J, Liebovitch L, West B (1994) Fractal physiology. Oxford University Press, New York

    Google Scholar 

  14. 14.

    Bourke P (2007) Self similarity. Fractals, Chaos URL

  15. 15.

    Calinon S, Billard A (2005) Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM. In: ICML ’05: Proceedings of the 22nd international conference on machine learning, pp. 105–112. ACM. doi:10.1145/1102351.1102365

  16. 16.

    Carlin M (2000) Measuring the complexity of non-fractal shapes by a fractal method. Patt Recog Lett 21(11):1013–1017

    Article  Google Scholar 

  17. 17.

    Chen B, Wang N (2000) Determining EMG embedding and fractal dimensions and its application. In: Engineering in medicine and biology society, 2000. Proceedings of the 22nd annual international conference of the IEEE 2:1341–1344

  18. 18.

    Christodoulou CI, Pattichis CS (1999) Unsupervised pattern recognition for the classification of EMG signals. IEEE Trans Biomed Eng 46(2):169–178

    PubMed  Article  CAS  Google Scholar 

  19. 19.

    Coatrieux JL, Toulouse P, Rouvrais B, Bars RL (1983) Automatic classification of electromyographic signals. EEG Clin Neurophysiol 55:333–341

    Article  CAS  Google Scholar 

  20. 20.

    Cram J, Kasman G, Holtz J (1998) Introduction to surface electromyography. Aspen Publishers Inc., Gaithersburg, Maryland

    Google Scholar 

  21. 21.

    Crawford B, Miller K, Shenoy P, Rao R (2005) Real-time classification of electromyographic signals for robotic control. Tech rep, University of Washington

  22. 22.

    De Luca C (2006) Electromyography. Encyclopedia of medical devices and instrumentation. Wiley, Indianapolis, pp. 98–109

  23. 23.

    Devaney RL (1995) Chaos in the classroom. Mathematics and statistics at Boston University. URL

  24. 24.

    Djuwari D, Kumar D, Raghupati S, Polus B (2003) Multi-step independent component analysis for removing cardiac artefacts from back sEMG signals. In: ANZIIS, pp. 35–40

  25. 25.

    Duchêne J, Goubel F (1993) Surface electromyogram during voluntary contraction: processing tools and relation to physiological events. Crit Rev Biomed Eng 21(4):313–397

    PubMed  Google Scholar 

  26. 26.

    Durgam V, Fernandes G, Preiszl H, Lutzenberger W, Pulvermuller F, Birbaumer N (1997) Fractal dimensions of short eeg time series in humans. Neurosci Lett 225(2):77–80. doi:10.1109/TNSRE.2007.908376

    Article  Google Scholar 

  27. 27.

    Enderle J, Blanchard SM, Bronzino J (eds) (2005) Introduction to Biomedical Engineering, 2nd edn. Academic Press, New York

    Google Scholar 

  28. 28.

    Englehart K, Hudgins B (2003) A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 50(7):848–854

    PubMed  Article  Google Scholar 

  29. 29.

    Esteller R, Vachtsevanos G, Echauz J, Litt B (2001) A comparison of waveform fractal dimension algorithms. Circuits and systems I: fundamental theory and applications, IEEE Transactions on see also Circuits and Systems I: Regular Papers, IEEE Trans on. 48(2):177–183. doi:10.1109/81.904882

    Google Scholar 

  30. 30.

    Falconer K (1990) Fractal geometry—mathematical foundations and applications. Wiley, New York

    Google Scholar 

  31. 31.

    Farina D, Merletti R, Stegeman DF (2005) Biophysics of the Generation of EMG Signals, Electromyography. Wiley-IEEE Press, USA, pp. 81–105

  32. 32.

    Feder J (1988) Fractals. Plenum Press, New York

    Google Scholar 

  33. 33.

    Gitter JA, Czerniecki MJ (1995) Fractal analysis of the electromyographic interference pattern. J Neurosci Method 58(1):103–108. doi:10.1016/0165-0270(94)00164-C

    Google Scholar 

  34. 34.

    Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220 . Circulation Electronic Pages:

  35. 35.

    Graupe D, Cline WK (1975) Functional separation of sEMG signals via arma identification methods for prosthesis control purposes. IEEE Trans Sys, Man, Cybern 5(2):252–259

    Google Scholar 

  36. 36.

    Green ER (1998) Understanding fractals and fractal dimensions. Senior honor thesis—University of Wisconsin, Madison, WI. URL

  37. 37.

    Gupta V, Suryanarayanan S, Reddy NP (1997) Fractal analysis of surface EMG signals from the biceps. Int J Med Inform 45(3):185–192. doi:10.1016/S1386-5056(97)00029-4

    Google Scholar 

  38. 38.

    Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography-151; theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Modern Phys 65(2):413–497

    Article  Google Scholar 

  39. 39.

    Hansen (2000) Blind separation of noicy image mixtures. Springer, Berlin, pp. 159–179

    Google Scholar 

  40. 40.

    He T, Clifford G, Tarassenko L (2006) Application of independent component analysis in removing artefacts from the electrocardiogram. Neural Comput Appl 15(2):105–116 doi:10.1007/s00521-005-0013-y

    Article  Google Scholar 

  41. 41.

    Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Phys D 31(2):277–283

    Article  Google Scholar 

  42. 42.

    Hillyard SA, Galambos R (1970) Eye movement artefact in the cnv. Electroencephalogr Clin Neurophysiol 28(2):173–182

    PubMed  Article  CAS  Google Scholar 

  43. 43.

    Hu X, Wang ZZ, Ren XM (2005) Classification of surface EMG signal with fractal dimension. J Zhejiang Univ Sci B 6(8):844–848

    PubMed  Article  Google Scholar 

  44. 44.

    Hu Y, Mak J, Liu H, Luk KDK (2007) Ecg cancellation for surface electromyography measurement using independent component analysis. In: Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on :3235–3238. doi:10.1109/ISCAS.2007.378161

  45. 45.

    Hyvarinen A, Cristescu R, Oja E (1999) A fast algorithm for estimating overcomplete ICA bases for image windows. In: Neural networks, 1999. IJCNN ’99. International joint conference on 2:894–899. doi:10.1109/IJCNN.1999.831071

  46. 46.

    Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley-Interscience, London

    Book  Google Scholar 

  47. 47.

    Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4-5):411–430

    PubMed  Article  Google Scholar 

  48. 48.

    Iannaconne P, Khokha M (1996) Fractal geometry in biological systems: an analytical approach. CRC Press, Boca Raton

    Google Scholar 

  49. 49.

    Ivanov PC, Amaral LAN, Goldberger AL, Stanley HE (1998) Stochastic feedback and the regulation of biological rhythms. EPL (Europhys Lett) 43(4):363–368

    Article  CAS  Google Scholar 

  50. 50.

    Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ (2001) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(2):163–178

    Article  Google Scholar 

  51. 51.

    Kabn (2000) Clustering of text documents by skewness maximization. pp. 435–440

  52. 52.

    Kalden R, Ibrahim S (2004) Searching for self-similarity in gprs. In: PAM 2004 : passive and active network measurement, pp. 83–92

  53. 53.

    Karlsson S, Yu J, Akay M (2000) Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study. IEEE Trans Biomed Eng 47(2):228–238 doi:10.1109/10.821766

    PubMed  Article  CAS  Google Scholar 

  54. 54.

    Kato M, Chen YW, Xu G (2006) Articulated hand tracking by pca-ica approach. In: FGR ’06: Proceedings of the 7th international conference on automatic face and gesture Recognition, IEEE Computer Society, pp. 329–334. doi:10.1109/FGR.2006.21

  55. 55.

    Katz MJ (1988) Fractals and the analysis of waveforms. Comp Biol Med 18(3):145–156

    Article  CAS  Google Scholar 

  56. 56.

    Kimura J (2001) Electrodiagnosis in diseases of nerve and muscle: principles and practice, 3rd edn. Oxford University Press, New York

    Google Scholar 

  57. 57.

    Kleine BU, van Dijk JP, Lapatki BG, Zwarts MJ, Stegeman DF (2007) Using two-dimensional spatial information in decomposition of surface EMG signals. J Electromyogr Kinesiol 17(5):535–548

    PubMed  Article  Google Scholar 

  58. 58.

    Knox R, Brooks D (1994) Classification of multifunction surface EMG using advanced ar model representations. Proceedings of the 20th annual northeast bioengineering conference, pp. 96–98

  59. 59.

    Kobayashi M, Musha T (1982) 1/f fluctuation of heartbeat period. IEEE Trans Biomed Eng BME-29(6):456–457 doi:10.1109/TBME.1982.324972

    Article  Google Scholar 

  60. 60.

    Kolenda (2000) Independent components in text. Advances in independent component analysis. Springer, Berlin, pp. 229–250

  61. 61.

    Kumar D, Pah ND (2000) Neural networks and wavelet decomposition for classification of surface electromyography. Electromyogr Clin Neurophysiol 40(6):411–421

    PubMed  CAS  Google Scholar 

  62. 62.

    Kumar DK, Ma N, Burton P (2001) Classification of dynamic multi-channel electromyography by neural network. Electromyogr Clin Neurophysiol 41(7):401–408

    PubMed  CAS  Google Scholar 

  63. 63.

    Lee TW (1997) Blind separation of delayed and convolved sources. pp. 758–764

  64. 64.

    Lee TW (1998) Independent component analysis: theory and applications. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  65. 65.

    Lee TW, Lewicki MS, Sejnowski TJ (1999) Unsupervised classification with non-gaussian mixture models using ica. In: Proceedings of the 1998 conference on advances in neural information processing systems, MIT Press, Cambridge, MA, USA, pp. 508–514

  66. 66.

    Lévy-Véhel J, Lutton E (2006) Fractals in Engineering; New Trends in Theory and Applications. Springer,New York Inc., Secaucus, NJ, USA

    Google Scholar 

  67. 67.

    Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12(2):337–365

    PubMed  Article  CAS  Google Scholar 

  68. 68.

    Lowery MM, O’Malley MJ (2003) Analysis and simulation of changes in EMG amplitude during high-level fatiguing contractions. IEEE Trans Biomed Eng 50(9):1052–1062

    PubMed  Article  Google Scholar 

  69. 69.

    Mackay DJC (1996) Maximum likelihood and covariant algorithms for independent component analysis. Tech. rep., University of Cambridge, London

  70. 70.

    Mandelbrot BB (1977) Fractals: Form, chance, and dimension, 1st edn. W. H. Freeman and Co, San Francisco

    Google Scholar 

  71. 71.

    Mckeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ (1999) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Map 6(3):160–188

    Article  Google Scholar 

  72. 72.

    Mckeown MJ, Torpey DC, Gehm WC (2002) Non-invasive monitoring of functionally distinct muscle activations during swallowing. Clin Neurophysiol 113(3):354–366. doi:10.1016/S1388-2457(02)00007-X

    PubMed  Article  Google Scholar 

  73. 73.

    Merletti R, Rainoldi A, Farina D (2005) Myoelectric manifestations of muscle fatigue, Electromyography, Wiley-IEEE Press, USA pp. 233–253

  74. 74.

    Momen K, Krishnan S, Chau T (2007) Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Trans Neu Sys Rehab Eng 15(4):535–542. doi:10.1109/TNSRE.2007.908376

    Article  Google Scholar 

  75. 75.

    Mosher JC, Lewis PS, Leahy RM (1992) Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng 39(6):541–557 doi:10.1109/10.141192

    PubMed  Article  CAS  Google Scholar 

  76. 76.

    Nagata K, Adno K, Magatani K, Yamada M (2005) A classification method of hand movements using multi channel electrode. 27th Annual international conference of the engineering in medicine and biology society, pp. 2375–2378

  77. 77.

    Nagata K, Magatani K (2004) Development of the assist system to operate a computer for the disabled using multichannel surface EMG. Proceedings of 26th annual international conference of the IEEE engineering in medicine and biology society

  78. 78.

    Naik G, Kumar D (2010) Identification of hand and finger movements using multi run ICA of surface electromyogram. J Med Sys pp. 1–11–11. doi:10.1007/s10916-010-9548-2

  79. 79.

    Naik GR, Kumar D (2010) Hybrid feature selection for myoelectric signal classification using MICA. J Elec Eng 1(2):93–99

    Article  Google Scholar 

  80. 80.

    Naik GR, Kumar DK (2010) Jayadeva: Twin svm for gesture classification using the surface electromyogram. IEEE Trans Infor Technol Biomed 14(2):301–308 doi:10.1109/TITB.2009.2037752

    Article  Google Scholar 

  81. 81.

    Naik GR, Kumar DK, Palaniswami M (2008) Identification of independent biological sensors-electromyogram example. pp. 1112–1115. IEEE. doi:10.1109/IEMBS.2008.4649355

  82. 82.

    Naik GR, Kumar DK, Palaniswami M (2008) Source identification and separation using sub-band ICA of sEMG. In: TENCON 2008—2008 IEEE Region 10 Conference, pp. 1–6. IEEE. doi:10.1109/TENCON.2008.4766726 URL

  83. 83.

    Naik GR, Kumar DK, Singh VP, Palaniswami M (2006) Hand gestures for hci using ICA of EMG. In: VisHCI ’06: Proceedings of the HCSNet workshop on Use of vision in human-computer interaction, Australian Computer Society, Inc., Sydney, Australia, pp. 67–72

  84. 84.

    Naik GR, Kumar DK, Weghorn H, Palaniswami M (2007) Subtle hand gesture identification for hci using temporal decorrelation source separation bss of surface EMG. In: Digital image computing techniques and applications, 9th Biennial conference of the Australian pattern recognition society, Adelaide, Australia, pp. 30–37

  85. 85.

    Naik GR, Kumar DK, Wheeler K, Arjunan SP (2009) Estimation of muscle fatigue during cyclic contractions using source separation techniques. In: 2009 Digital image computing: techniques and applications, Melbourne, Australia, pp. 217–222. doi:10.1109/DICTA.2009.43

  86. 86.

    Naik GR, Kumar DK, Yadav V, Wheeler K, Arjunan S (2009) Testing of motor unit synchronization model for localized muscle fatigue. In: 2009 Annual international conference of the IEEE engineering in medicine and biology society, Minneapolis, USA, pp. 360–363. doi:10.1109/IEMBS.2009.5332486

  87. 87.

    Nakamura H, Yoshida M, Kotani M, Akazawa K, Moritani T (2004) The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part i-measuring techniques. J Electromyogr Kinesiol: Official J Int Soc Electrophysiol Kinesiol 14(4):423–432. doi:10.1016/j.jelekin.2004.01.004

    Google Scholar 

  88. 88.

    Niedermeyer E, Da Silva FL (1999) Electroencephalography: basic principles, clinical applications, and related fields, 4th edn. Lippincott Williams and Wilkins, Philadelphia, PA

    Google Scholar 

  89. 89.

    Nussbaum MA (2006) Localized muscle fatigue. Lecture notes on advanced methods in occupational biomechanics. URL

  90. 90.

    Osamu Fukuda TT (2004) Control of an externally powered prosthetic forearm using raw-EMG signals. SICE 40(11):1124–1131

    Google Scholar 

  91. 91.

    Parra J, Kalitzin SN (2004) Lopes: magnetoencephalography: an investigational tool or a routine clinical technique?. Epilepsy Behav 5(3):277–285

    PubMed  Article  Google Scholar 

  92. 92.

    Peng CK, Hausdorff J, Goldberger A (1999) Fractal mechanisms in neural control: human heartbeat and gait dynamics in health and disease. Nonlinear Dynamics, Self-Organization, and biomedicine, Cambridge University Press, Cambridge

  93. 93.

    Peters J (1967) Surface electrical fields generated by eye movement and eye blink potentials over the scalp. J EEG Technol 7:1129–1159

    Google Scholar 

  94. 94.

    Petersen K, Hansen LK, Kolenda T, Rostrup E (2000) On the independent components of functional neuroimages. In: 3rd International conference on independent component analysis and blind source separation, pp. 615–620

  95. 95.

    Petrosian A (1995) Kolmogorov complexity of finite sequences and recognition of different preictal eeg patterns. In: Computer-based medical systems, Proceedings of the 8th IEEE symposium on, pp. 212–217

  96. 96.

    Rainoldi A, Casale R, Hodges P, Jull G (2005) Applications in rehabilitation medicine and related fields, Electromyography, Wiley-IEEE Press, New York, pp. 403–425

  97. 97.

    Rajapakse JC, Cichocki A (2002) Sanchez: Independent component analysis and beyond in brain imaging: EEg, MEG, fMRI, and PEt. In: Neural information processing, 2002. ICONIP ’02. Proceedings of the 9th international conference on 1:404–412. doi:10.1109/ICONIP.2002.1202202

  98. 98.

    Ren XH, Wang Z, Yan Z (2006) Muap extraction and classification based on wavelet transform and ICA for EMG decomposition. Med Biol Eng Comput 44:371–382

    PubMed  Article  Google Scholar 

  99. 99.

    Sarkar M, Leong TY (2003) Characterization of medical time series using fuzzy similarity-based fractal dimensions. Artif Intell Med 27(2):201–222

    PubMed  Article  Google Scholar 

  100. 100.

    Scherg M, Von Cramon D (1985) Two bilateral sources of the late aep as identified by a spatio-temporal dipole model. Electroencephalogr Clin Neurophysiol 62(1):32–44

    PubMed  Article  CAS  Google Scholar 

  101. 101.

    Schroeder M (1991) Fractals, chaos, power laws. Freeman, New York

    Google Scholar 

  102. 102.

    Sorenson (2002) Mean field approaches to independent component analysis. Neu Comput 14:889–918

    Article  Google Scholar 

  103. 103.

    Stashuk D (2001) EMG signal decomposition: how can it be accomplished and used?. J Electromyogr Kinesiol 11(3):151–173

    PubMed  Article  CAS  Google Scholar 

  104. 104.

    Subasi A, Kiymik M (2010) Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks. J Med Sys 34(4):777–785. doi:10.1007/s10916-009-9292-7

    Article  Google Scholar 

  105. 105.

    Sueaseenak D, Chanwimalueang T, Praliwanon C, Sangworasil M, Pintavirooj C (2009) An eigen based feature on time-frequency representation of EMG. In: 2009 IEEE-RIVF international conference on computing and communication technologies, pp. 1–6. doi:10.1109/RIVF.2009.5174621

  106. 106.

    Tang AC, Pearlmutter BA (2003) Independent components of magnetoencephalography: localization pp. 129–162

  107. 107.

    Tenore F, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor N (2009) Decoding of individuated finger movements using surface electromyography. IEEE Trans Biomed Eng 56(5):1427–1434. doi:10.1109/TBME.2008.2005485

    PubMed  Article  Google Scholar 

  108. 108.

    Tsuji T, Kaneko M (2000) An EMG controlled pointing device using a neural network. SICE 37(5):425–431

    Google Scholar 

  109. 109.

    Verleger R, Gasser T, Mocks J (1982) Correction of eog artefacts in event related potentials of the eeg: aspects of reliability and validity, psychophysiology. Psychophysiology 19(2):472–480

    PubMed  Article  CAS  Google Scholar 

  110. 110.

    Vigário R, Särelä J, Jousmäki V, Hämäläinen M, Oja E (2000) Independent component approach to the analysis of eeg and MEG recordings. IEEE Trans Biomed Eng 47(5):589–593 doi:10.1109/10.841330

    PubMed  Article  Google Scholar 

  111. 111.

    Weerts TC, Lang PJ (1973) The effects of eye fixation and stimulus and response location on the contingent negative variation (cnv). Biol Psychol 1(1):1–19

    PubMed  Article  CAS  Google Scholar 

  112. 112.

    Whitton JL, Lue F, Moldofsky H (1978) A spectral method for removing eye movement artifacts from the eeg. Electroencephalogr Clin Neurophysiol 44(6):735–741

    PubMed  Article  CAS  Google Scholar 

  113. 113.

    Wisbeck J, Barros A, Ojeda R (1998) Application of ICA in the separation of breathing artifacts in ECG signals

  114. 114.

    Woestenburg JC, Verbaten MN, Slangen JL (1983) The removal of the eye-movement artifact from the eeg by regression analysis in the frequency domain. Biol Psychol 16(1-2):127–147

    PubMed  Article  CAS  Google Scholar 

  115. 115.

    Xu Z, Xiao S (1997) Fractal dimension of surface EMG and its determinants. In: Engineering in medicine and biology society, 1997. Proceedings of the 19th annual international conference of the IEEE 4:1570–1573

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Naik, G.R., Arjunan, S. & Kumar, D. Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review. Australas Phys Eng Sci Med 34, 179–193 (2011).

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  • Independent component analysis
  • Blind source separation
  • Fractal theory
  • Fractal dimension
  • Surface electromyography