A person with a spinal cord injury or a lost limb could one day control a prosthetic hand by having an ultrasound machine watch their muscles twitch. Today’s neural interfaces rely on surface electromyography (EMG), which picks up electrical signals from only the most superficial muscles. This works poorly for many patients, because the signals are biased toward people with specific body characteristics that most amputees and spinal-cord-injury patients do not share. The result is a technology that often fails in the clinic. This project aims to replace EMG with ultrafast 3D ultrasound. The idea is to track tiny movements inside a contracting muscle and use those movements as a proxy to identify which spinal motoneurons are firing. The researchers will first develop algorithms that compensate for probe shifts and track muscle deformation in real time. They will then test the system on healthy volunteers and on people with trans-radial amputations, using standardised hand movements as the benchmark. If it works, the approach could give amputees and paralysed individuals a more natural, reliable way to control prostheses or exoskeletons—without surgery, without implanted electrodes, and without the limitations of surface EMG.
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Human movement relies on the communication between the brain and the muscles via the nerves. Suppose this communication is limited or non-existent due to peripheral nerve trauma. In that case, performing daily tasks will be difficult or impossible. To simplify or enable communication, efforts have been put into recording biological signals from the limb to communicate with a robotic arm or machine, i.e., a neural interface. A neural interface can rehabilitate people with spinal cord injuries, control upper limb prostheses, and assist people using exoskeletons. Today's neural interfaces are based on detecting spinal motoneuron activity with surface electromyography (EMG). However, surface EMG only detects superficial muscle activity and is biased towards subjects with characteristics rarely coinciding with most patient categories. Thus limiting their usefulness and applicability. Therefore, we must shift the focus to other techniques that can provide a natural neural interface that overcomes such limitations. Preferably a technique that could potentially interface all motoneurons innervating the muscle. This proposal aims to develop a neural interface between spinal motoneurons and 3D ultrafast ultrasound using local muscle movements as a proxy for identifying neural activity. For this purpose, methods will be developed, and the technology will be demonstrated using upper limb prostheses as a representative case study. The three specific objectives are: 1) To develop and evaluate a method that compensates probe shifts and tracks muscle deformation in ultrafast ultrasound imaging during muscle contractions. 2) To develop and evaluate a robust decomposition method of ultrafast ultrasound images for real-time motoneuron identification. 3) To evaluate the precision of the neural interface using standardised hand movements from healthy subjects and trans-radial amputees as a representative application scenario.
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