Computed myography: three-dimensional reconstruction of motor functions from surface EMG data

We describe a methodology to qualitatively and quantitatively determine the activation level of individual muscles by voltage measurements from an array of voltage sensors on the skin surface. A physical rnite element model for electrostatics simulation is constructed from morphometric data and numerical inversion techniques are used to determine muscle activation patterns. Preliminary results from experiments with simulated and human data are presented for activation reconstructions of three muscles in the upper arm (biceps brachii, bracialis, and triceps). This approach potentially offers a new clinical tool to sensitively assess muscle function in patients suffering from neurological disorders (e.g., spinal cord injury) and could more accurately guide advances in the evaluation of specific rehabilitation training regimens.

[1]  H. Helmholtz Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern mit Anwendung auf die thierisch‐elektrischen Versuche , 1853 .

[2]  A. Beardwell Electromyography , 1945 .

[3]  P. London Injury , 1969, Definitions.

[4]  P. Rosenfalck Intra- and extracellular potential fields of active nerve and muscle fibres. A physico-mathematical analysis of different models. , 1969, Acta physiologica Scandinavica. Supplementum.

[5]  C. Vogel Computational Methods for Inverse Problems , 1987 .

[6]  R. Greenblatt Probabilistic reconstruction of multiple sources in the bioelectromagnetic inverse problem , 1993 .

[7]  V. L. Stonick,et al.  Processing signals from surface electrode arrays for noninvasive 3D mapping of muscle activity , 1994, Proceedings of IEEE 6th Digital Signal Processing Workshop.

[8]  I F Gorodnitsky,et al.  Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. , 1995, Electroencephalography and clinical neurophysiology.

[9]  E.F. LoPresti,et al.  Identifying significant frequencies in surface EMG signals for localization of neuromuscular activity , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[10]  J. T. Stonick,et al.  Estimation and localization of multiple dipole sources for noninvasive mapping of muscle activity , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[11]  S. Gonzalez-Andino,et al.  A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem , 1998, IEEE Transactions on Biomedical Engineering.

[12]  W. Donovan,et al.  Neurologic recovery after traumatic spinal cord injury: data from the Model Spinal Cord Injury Systems. , 1999, Archives of physical medicine and rehabilitation.

[13]  Kevin C. McGill,et al.  A model of the muscle-fiber intracellular action potential waveform, including the slow repolarization phase , 2001, IEEE Trans. Biomed. Eng..

[14]  E. Chauvet,et al.  Inverse problem in the surface EMG: a feasibility study , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[16]  R N Lemon,et al.  A novel algorithm to remove electrical cross‐talk between surface EMG recordings and its application to the measurement of short‐term synchronisation in humans , 2002, The Journal of physiology.

[17]  Todd A. Kuiken,et al.  A multiple-layer finite-element model of the surface EMG signal , 2002, IEEE Transactions on Biomedical Engineering.

[18]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[19]  D F Stegeman,et al.  A thin, flexible multielectrode grid for high-density surface EMG. , 2004, Journal of applied physiology.

[20]  V. Dietz,et al.  Providing the clinical basis for new interventional therapies: refined diagnosis and assessment of recovery after spinal cord injury , 2004, Spinal Cord.

[21]  P. Ellaway,et al.  Towards improved clinical and physiological assessments of recovery in spinal cord injury: a clinical initiative , 2004, Spinal Cord.

[22]  Roberto Merletti,et al.  Electromyography. Physiology, engineering and non invasive applications , 2005 .

[23]  Joshua C. Kline,et al.  Decomposition of surface EMG signals. , 2006, Journal of neurophysiology.

[24]  V. Dietz,et al.  Neurological aspects of spinal-cord repair: promises and challenges , 2006, The Lancet Neurology.

[25]  Uri M. Ascher,et al.  On level set regularization for highly ill-posed distributed parameter estimation problems , 2006, J. Comput. Phys..

[26]  S.B. Giordano,et al.  Leg Muscles Differ in Spatial Activation Patterns with Differing Levels of Voluntary Plantarflexion Activity in Humans , 2006, Cells Tissues Organs.

[27]  B. Dobkin,et al.  Cellular Transplants in China: Observational Study from the Largest Human Experiment in Chronic Spinal Cord Injury , 2006, Neurorehabilitation and neural repair.

[28]  T. Fukunaga,et al.  Quantitative assessment of skeletal muscle activation using muscle functional MRI. , 2006, Magnetic resonance imaging.

[29]  Gea Drost,et al.  Clinical applications of high-density surface EMG: a systematic review. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[30]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[31]  U. Ascher,et al.  Dynamic level set regularization for large distributed parameter estimation problems , 2007 .

[32]  E. Bizzi,et al.  Article history: , 2005 .