Decoding a New Neural–Machine Interface for Control of Artificial Limbs

Abstract

An analysis of the motor control information content made available with a neural-machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI-called targeted muscle reinnervation (TMR)-to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved.

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

“…For example, using classic EMG features, a classification accuracy of 98% and 93% was previously reported for two patients and 8 classes 30 , which is a similar result as obtained in the present study for patients T1 and T2 for 7 and 9 classes, respectively. In our experiment, however, the performance with classic EMG features degraded for patient T3 when classifying 11 classes and was lower than that previously reported in four patients for 16 classes 19 . However, three of the four patients in this previous study were transhumeral amputees with physiological innervation of the lateral heads of biceps and triceps and the window duration used for the analysis (256 ms) was longer than in our study (100 ms).…”

“…The classification results indicated an almost perfect discrimination (on average >97%) of up to 11 classes. The results obtained from global EMG analysis in this study are partly in agreement with those reported in previous studies (e.g., 15,19,30 ). However, given the relatively small number of available TMR patients in the current and previous studies and differences in the experimental and analysis conditions, comparisons between studies are difficult.…”

“…Rather, this interface uses the EMG as an interferent signal (colored noise) from which few global features are extracted 18 , e.g. amplitude or spectral moments, for either direct control or for identifying sets of predefined movements 19 . This global approach is common to previous TMR investigations, even when more advanced (multi-channel) EMG systems have been employed 19 .…”

“…amplitude or spectral moments, for either direct control or for identifying sets of predefined movements 19 . This global approach is common to previous TMR investigations, even when more advanced (multi-channel) EMG systems have been employed 19 . This procedure limits the decoding quality of the neural interface because the global EMG is a spatio-temporal summation of action potentials that creates correlations amongst multiple channels and therefore determines an ill posed inverse problem for decoding 20 .…”

Identificación de Tareas Isométricas y Dinámicas del Miembro Superior Basada en EMG de Alta Densidad

“…Para la identificación de diferentes tipos o intenciones de movimiento se utilizó un clasificador lineal basado en análisis discriminante (LDA; linear discriminant analysis), al tratarse de una de las técnicas más utilizadas por su rapidez y que puede ser fácilmente implementado en un sistema real (Zhou et al, 2007).…”