Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques

Open access

Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques

The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Basmajian J. V. De Luca C. J. (1985). Muscles Alive: Their Functions Revealed by Electromyography (5th ed.). Baltimore US: Williams & Wilkins Publishers.

  • Schlenzig J. Hunter E. Jain R. (1994). Vision based hand gesture interpretation using recursive estimation. In Twenty-Eighth Asilomar Conference on Signals Systems and Computers. IEEE 1264-1271.

  • Cheron G. Draye J. P. Bourgeios M. Libert G. (1996). A dynamic neural network identification of electromyography and arm trajectory relationship during complex movements. IEEE Trans. Biomed. Eng. 43 552-558.

  • Pavlovic V. I. Sharma R. Huang T. S. (1997). Visual interpretation of hand gestures for human computer interaction. IEEE Trans. Pattern Anal. Mach. Intell. 19 677-695.

  • Wheeler K. R. Jorgensen C. C. (2003). Gestures as input: neuroelectric joysticks and keyboards. IEEE Pervasive Comput. 2 (2) 56-61.

  • Koike Y. Kawato M. (1996). Human interface using surface electromyography signals. Electron. Comm. Jpn. 79 (9) 15-22.

  • Doerschuk P. C. Gustafson D. E. Willsky A. S. (1983). Upper extremity limb function discrimination using EMG signal analysis. IEEE Trans. Biomed. Eng. 30(1) 18-28.

  • Farry K. A. Walker I. D. Baraniuk R. G. (1996). Myoelectric teleoperation of a complex robotic hand. IEEE Trans. Robot. Autom. 12 (5) 775-788.

  • McKeown M. J. (2000). Cortical activation related to arm-movement combinations. Muscle Nerve 23 (9) S19-S25.

  • McKeown M. J. Radtke R. (2001). Phasic and tonic coupling between EEG and EMG demonstrated with independent component analysis. J. Clin. Neurophysiol. 18 (1) 45-57.

  • Nakamura H. Yoshida M. Kotani M. Akazawa K. Moritani T. (2004). The application of independent component analysis to the multichannel surface electromyographic signals for separation of motor unit action potential trains. J. Electromyogr. Kinesiol. 14 (4) 423-432.

  • Peleg D. Braiman E. Yom-Tov E. Inbar G. F. (2002). Classification of finger activation for use in a robotic prosthesis arm. IEEE Trans. Neural Sys. Rehab. Eng. 10 (4) 290-293.

  • Naik G. R. Kumar D. K. Singh V. P. Palaniswami M. (2006). SEMG for identifying hand gestures using ICA. In Workshop on Biosignal Processing and Classification at 2nd International Conference on Informatics in Control Automation and Robotics August 2006. Setubal Portugal 61-67.

  • Hyvarinen A. Karhunen J. Oja E. (2001). Independent Component Analysis (3rd ed.). New York: John Wiley.

  • Bell A. Sejnowski T. (1995). An information - maximisation approach to blind separation and blind deconvolution. Neural Comput. 7 1129-1159.

  • Jung T. Makeig S. Lee T. McKeon M. Brown G. Bell A. Sejnowski T. (2000). Independent component analysis of biomedical signals. In Second International Workshop on Independent Component Analysis and Blind Signal Separation 633-644.

  • Makeig S. Jung T. Bell A. Sejnowski T. (1996). Independent component analysis of electro-encephalographic data. Adv. Neural Inf. Proc. 8 145-151.

  • Naik G. R. Kumar D. K. Weghorn H. (2007). Performance comparison of ICA algorithms for isometric hand gesture identification using surface EMG. In 3rd International Conference on Intelligent Sensors Sensor Networks and Information Processing 3-6 December 2007 613-618.

  • Fridlund A. J. Cacioppo J. T. (1986). Guidelines for human electromyographic research. Psychophysiol. 23 (5) 567-589.

  • Hair J. F. Black W. C. Babin B. J. Anderson R. E. Tatham R. L. (2006). Multivariate Data Analysis. Prentice Hall.

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 1.122
5-year IMPACT FACTOR: 1.157

CiteScore 2018: 1.39

SCImago Journal Rank (SJR) 2018: 0.325
Source Normalized Impact per Paper (SNIP) 2018: 0.881

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 157 81 1
PDF Downloads 89 61 2