Large-scale intramuscular electrode system for chronic electromyography and functional electrical stimulation.

To understand how the central nervous system (CNS) enacts movements, it seems important to monitor the activities of the many muscles involved. Likewise, to restore complex movements to paralyzed limbs with electrical stimulation requires access to most limb muscles. Intramuscular electrodes are needed to obtain isolated recordings or stimulation of individual muscles. As such, we developed and tested the stability of large arrays of implanted intramuscular electrodes. We implanted 58 electrodes in 29 upper limb muscles in each of three macaques. Electrode connectors were protected within a skull-mounted chamber. During surgery, wires were tunneled subcutaneously to target muscles, where gold anchors were crimped onto the leads. The anchors were then deployed with an insertion device. In two monkeys, the chamber was fixed to the skull with a titanium baseplate rather than acrylic cement. In multiple sessions up to 15 wk after surgery, electromyographic (EMG) signals were recorded while monkeys made the same reaching movement. EMG signals were stable, with an average (SD) coefficient of variation across sessions of 0.24 ± 0.15. In addition, at 4, 8, and 16 wk after surgery, forces to incrementing stimulus pulses were measured for each electrode. The threshold current needed to evoke a response at 16 wk was not different from that at 4 wk. Likewise, peak force evoked by 16 mA of current at 16 wk was not different from 4 wk. The stability of this system implies it could be effectively used to monitor and stimulate large numbers of muscles needed to understand the control of natural and evoked movements.NEW AND NOTEWORTHY A new method was developed to enable long-lasting recording and stimulation of large numbers of muscles with intramuscular electrodes. Electromyographic signals and evoked force responses in 29 upper limb muscles remained stable over several months when tested in nonhuman primates. This system could be used effectively to monitor and stimulate numerous muscles needed to more fully understand the control of natural and evoked movements.

Restoration of complex movement in the paralyzed upper limb.

Objective.Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements.Approach. Arm kinematics and electromyographic (EMG) activity from 29 muscles were recorded while a 'trainer' monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys.Main results. Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed.Significance. Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.

Object discrimination using electrotactile feedback.

OBJECTIVE: A variety of bioengineering systems are being developed to restore tactile sensations in individuals who have lost somatosensory feedback because of spinal cord injury, stroke, or amputation. These systems typically detect tactile force with sensors placed on an insensate hand (or prosthetic hand in the case of amputees) and deliver touch information by electrically or mechanically stimulating sensate skin above the site of injury. Successful object manipulation, however, also requires proprioceptive feedback representing the configuration and movements of the hand and digits. APPROACH: Therefore, we developed a simple system that simultaneously provides information about tactile grip force and hand aperture using current amplitude-modulated electrotactile feedback. We evaluated the utility of this system by testing the ability of eight healthy human subjects to distinguish among 27 objects of varying sizes, weights, and compliances based entirely on electrotactile feedback. The feedback was modulated by grip-force and hand-aperture sensors placed on the hand of an experimenter (not visible to the subject) grasping and lifting the test objects. We were also interested to determine the degree to which subjects could learn to use such feedback when tested over five consecutive sessions. MAIN RESULTS: The average percentage correct identifications on day 1 (28.5% ± 8.2% correct) was well above chance (3.7%) and increased significantly with training to 49.2% ± 10.6% on day 5. Furthermore, this training transferred reasonably well to a set of novel objects. SIGNIFICANCE: These results suggest that simple, non-invasive methods can provide useful multisensory feedback that might prove beneficial in improving the control over prosthetic limbs.