Research Library

A mathematical model of possible mechanisms contributing to phantom limb pain. By modelling the activity of neurons in the spinal cord we can recreate several characteristics that are typical for pain. We then explore how the system behaves after a severe nerve injury, such as an amputation, and how this could contribute to phantom limb pain.

When we electrically stimulate the nerves of people with amputation, they can feel sensations on their missing limb, which we can use to provide them with feedback from their prosthetic hand. However, these electrical stimulations may also be picked up on the implanted sensors used to control the prosthesis. When this happens, these stimulations can sometimes cause the prosthesis to move uncontrollably or unpredictably. To address this issue, we developed and tested two stimulation artifact removal algorithms can learn to filter out these stimulations from the prosthesis control signals. In doing so, we demonstrated the ability to improve the quality of these control signals, as well as to improve the user’s ability to control their prosthetic hand simultaneously with nerve stimulation. Overall, this will improve our people’s experience when using sensory feedback at home to feel the objects they are interacting with, without having to sacrifice their control over their prostheses.

We developed a new method to calculate the strength of electrical connections between sensors implanted in the body, such as those used with our neuromusculoskeletal prosthesis. This method relies only on simple mathematics, and can work for any type and number of implanted sensors that share an electrical reference. Using this method, we are able to paint a more complete and accurate picture of how well our implanted sensors are working, as well as to more quickly pinpoint the cause of issues when they arise.

Robotic prostheses controlled by myoelectric signals can restore limited but important hand function in individuals with upper limb amputation. The lack of individual finger control highlights the yet insurmountable gap to fully replacing a biological hand. Implanted electrodes around severed nerves have been used to elicit sensations perceived as arising from the missing limb, but using such extra-neural electrodes to record motor signals that allow for the decoding of phantom movements has remained elusive. Here, we showed the feasibility of using signals from non-penetrating neural electrodes to decode intrinsic hand and finger movements in individuals with above-elbow amputations. We found that information recorded with extra-neural electrodes alone was enough to decode phantom hand and individual finger movements, and as expected, the addition of myoelectric signals reduced classification errors both in offline and in real-time decoding.

Working under the assumption that steady-state EMG signals are more prone to be affected by factors such as differing physiology and fatigue, we investigate the possibility of creating a classifier that acts on the transient EMG observed during muscle contraction. During evaluation we obtained performances comparable to state-of-the-art steady-state EMG controllers; illustrating the possibility of using the transient portion of EMG signals for classification.

Stroke is one of the leading causes of disability and patients do not receive sufficient rehabilitation to avoid permanent impairment. Here we present a new rehabilitation platform using serious games controlled with myoelectric pattern recognition which can potentially be made available in the home environment to increase the amount of rehabilitation. Preliminary testing shows promising results, and the platform will be used in a future trial.

Upper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation, but there is no collated evidence on the different sEMG-driven interventions and their effect on upper limb function in people with stroke. Synthesize existing evidence and perform a meta-analysis on the effect of different types of sEMG-driven interventions on upper limb function in people with stroke. PubMed, SCOPUS, and PEDro databases were systematically searched for eligible randomized clinical trials that utilize sEMG-driven interventions to improve upper limb function assessed by Fugl-Meyer Assessment (FMA-UE) in stroke. The PEDro scale was used to evaluate the methodological quality and the risk of bias of the included studies. In addition, a meta-analysis utilizing a random effect model was performed for studies comparing sEMG interventions to non-sEMG interventions and for studies comparing different sEMG interventions protocols. Twenty-four studies comprising 808 participants were included in this review. The methodological quality was good to fair. The meta-analysis showed no differences in the total effect, assessed by total FMA-UE score, comparing sEMG interventions to nonsEMG interventions (14 studies, 509 participants, SMD 0.14, P 0.37, 95% CI –0.18 to 0.46, I2 55%). Similarly, no difference in the overall effect was found for the meta-analysis comparing different types of sEMG interventions (7 studies, 213 participants, SMD 0.42, P 0.23, 95% CI –0.34 to 1.18, I2 73%). Twenty out of the twenty-four studies, including participants with varying impairment levels at all stages of stroke recovery, reported statistically significant improvements in upper limb function at post-sEMG intervention compared to baseline. This review and meta-analysis could not discern the effect of sEMG in comparison to a non-sEMG intervention or the most effective type of sEMG intervention for improving upper limb function in stroke populations. Current evidence suggests that sEMG is a promising tool to further improve functional recovery, but randomized clinical trials with larger sample sizes are needed to verify whether the effect on upper extremity function of a specific sEMG intervention is superior compared to other non-sEMG or other type of sEMG interventions.

When one user of our neuromusculoskeletal prostheses competed in the Cybathlon 2020, an international competition for advanced bionic limbs, he not only trained intensely during the months leading up to the competition, but also continued using his prosthesis more often and dexterously even after the competition had ended. By looking at data saved to a memory card in the prosthesis, we discovered that he learned to control his hand with more finesse, and the skills he gained from training carried over into his daily life as he uses his prosthetic hand, wrist, and elbow at home. When asking him about his experience, he told us “I don’t feel like I have one arm anymore,” and explained that he felt his self-confidence had improved from participating in the competiton. We hope that these results will encourage clinicians to use competitions like the Cybathlon to encourage their patients to train with their prosthetic limbs and to regain more of their lost function in their daily lives.

Phantom limb pain (PLP) is a complex medical condition that is often difficult to treat, and thus can become detrimental to patients’ quality of life. No standardized clinical treatments exist and there is no conclusive understanding of the underlying mechanisms causing it. Noninvasive brain stimulation (NIBS) has been used to find correlations between changes in brain activity and various brain conditions, including neurological disease, mental illnesses, and brain disorders. Studies have also shown that NIBS can be effective in alleviating pain. Here, we examined the literature on a particular type of NIBS, known as transcranial direct current stimulation (tDCS), and its application to the treatment of PLP.We first discuss the current hypotheses on theworkingmechanism of tDCS and then we examine published evidence of its efficacy to treat PLP. We conclude this article by discussing how tDCS alone, and in combination with brain imaging techniques such as electroencephalography (EEG) and magnetic resonance imagining, could be applied to further investigate the mechanisms underlyingPLP.

Electrodes used for measuring electric signals from the body are commonly made of metal making them expensive, stiff, non-efficient potentially toxic. We made electrodes made of graphene induced with a laser allows for an economical, soft, and organic electrode. We tested the graphene electrode on the bench and in humans and found that they were more stable in bench testing and rivals metal electrodes in human testing. Graphene electrodes show potential to replace metal electrodes leading to better and cheaper electrodes.