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A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular Emg Recordings Publisher Pubmed



Karimimehr S1, 2 ; Marateb HR1, 3 ; Muceli S4, 5 ; Mansourian M6 ; Mananas MA3, 7 ; Farina D8
Authors
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Authors Affiliations
  1. 1. Faculty of Engineering, Biomedical Engineering Department, University of Isfahan, HezarJerib st., Isfahan, 81746-73441, Iran
  2. 2. Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5746, Tehran, Iran
  3. 3. Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politecnica de Catalunya BarcelonaTech (UPC), Barcelona, Spain
  4. 4. Institute of Neurorehabilitation Systems, University Medical Center Gottingen, Georg-August University, Gottingen, Germany
  5. 5. Clinic for Trauma Surgery, Orthopaedics and Plastic Surgery, University Medical Center Gottingen, Gottingen, Germany
  6. 6. Department of Biostatistics and Epidemiology, School of Public Health, Isfahan University of Medical Sciences, HezarJerib St., Isfahan, Iran
  7. 7. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
  8. 8. Department of Bioengineering, Imperial College London, London, United Kingdom

Source: International Journal of Neural Systems Published:2017


Abstract

The neural command from motor neurons to muscles - sometimes referred to as the neural drive to muscle - can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was 0.5±0.8, 1.2±1.0, and 1.0±0.8, respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were 70±9%, and 94±5%, respectively. The time cost for processing each 200ms iEMG interval was 43±16 (21-97)ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was 74±18% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control. © 2017 The Author(s).
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