The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 22; no. 4; pp. 797 - 809
• Main Authors: Farina, Dario, Jiang, Ning, Rehbaum, Hubertus, Holobar, Ales, Graimann, Bernhard, Dietl, Hans, Aszmann, Oskar C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Action Potentials - physiology; Arm; Artificial Intelligence - trends; Artificial Limbs - trends; Crosstalk; Electric potential; Electrodes; Electromyography; Electromyography - trends; Feature extraction; Feedback, Physiological - physiology; Humans; Motor unit; Movement - physiology; Muscle Contraction - physiology; Muscle, Skeletal - physiology; Muscles; myoelectric control; neural drive to muscle; Neurons; pattern recognition; Pattern Recognition, Automated - trends; regression
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2014.2305111

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e, the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time (<;200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.