Abstract
Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals.
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References
McKeown, M. J., Torpey, D. C., Gehm W. C.: Non-Invasive Monitoring of Functionally Distinct Muscle Activations during Swallowing. Clinical Neurophysiology (2002).
McKeown, M. J.: Cortical activation related to arm movement combinations. Muscle Nerve. 9:19–25 (2000).
Jung T.P., Makeig S., McKeown M.J., Bell A.J., Lee T.W., Sejnowski T. J.: Imaging bra-indynamics using independent component analysis. Proc. IEEE. 89(7): 1107–22, (2001).
Bell A. J., Sejnowski T. J.: An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 7:1129–1159, (1995).
Micera S., Vannozzi G., Sabatini A. M., Dario P.: Improving Detection of Muscle Activation intervals, IEEE Engineering in Medicine and Biology, vol. 20 n. 6:38–46 (2001).
Karhunen J., Oja E.: A Class of Neural Networks for Independent Component Analysis, IEEE Transactions on Neural Network, vol. 8 n. 3:486–504, (1997).
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© 2002 Springer-Verlag Berlin Heidelberg
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Azzerboni, B., Finocchio, G., Ipsale, M., La Foresta, F., Morabito, F.C. (2002). A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_11
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DOI: https://doi.org/10.1007/3-540-45808-5_11
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