A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings.
Por:
Karimimehr S, Marateb HR, Muceli S, Mansourian M, Mañanas MA and Farina D
Publicada:
1 sep 2017
Ahead of Print:
20 mar 2017
Resumen:
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 [Formula: see text], [Formula: see text], and [Formula: see text], 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 [Formula: see text], and [Formula: see text], respectively. The time cost for processing each 200[Formula: see text]ms iEMG interval was [Formula: see text] (21-97)[Formula: see text]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 [Formula: see text]% 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.
Filiaciones:
Karimimehr S:
* Faculty of Engineering, Biomedical Engineering Department, University of Isfahan, HezarJerib st., 81746-73441 Isfahan, Iran
† Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5746 Tehran, Iran
Marateb HR:
* Faculty of Engineering, Biomedical Engineering Department, University of Isfahan, HezarJerib st., 81746-73441 Isfahan, Iran
‡ Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya BarcelonaTech (UPC), 08028 Barcelona, Spain
Muceli S:
§ Institute of Neurorehabilitation Systems, University Medical Center Göttingen, Georg-August University, 37075 Göttingen, Germany
¶ Clinic for Trauma Surgery, Orthopaedics and Plastic Surgery, University Medical Center Göttingen, Göttingen, Germany
Mansourian M:
? Department of Biostatistics and Epidemiology, School of Public Health, Isfahan University of Medical Sciences, HezarJerib St., 81745 Isfahan, Iran
Mañanas MA:
‡ Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya BarcelonaTech (UPC), 08028 Barcelona, Spain
** Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 08028, Barcelona, Spain
Farina D:
†† Department of Bioengineering, Imperial College London, SW7 2AZ London, UK
Open Access
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