University of Cambridge > > Computational Neuroscience > Wearable non-invasive human neural interface with action potential resolution

Wearable non-invasive human neural interface with action potential resolution

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If you have a question about this talk, please contact Guillaume Hennequin.

As the nervous system’s evolved output, spinal motor neuron activity is from an evolutionary perspective an appropriate source of signals for a neural interface, and humans can learn to independently activate multiple individual motor neurons innervating the same muscle. Furthermore, the reliable amplification of motor neuron action potentials by muscle fibers allows them to be measured non-invasively with surface electromyography (sEMG). We have developed a novel wearable wireless system for state-of-the-art dry electrode sEMG recording from the human wrist and forearm without the need for any skin preparation. Using this system, we demonstrate real-time detection and identification of individual motor unit action potentials, each of which corresponds to the firing of an individual spinal motor neuron. This ability to monitor spiking activity of individual neurons sets sEMG apart from other non-invasive measurements of neural activity. From the recorded sEMG signals, we develop personalized discrete and continuous control schemes, and we also compute real-time predictions of joint angles, muscle tensions, and forces of the wrist and hand. Relative to traditional human-computer interfaces, neuromotor interfaces have the potential to increase human-to-computer communication bandwidth, and substantial reductions in latency are also achievable because sEMG signals precede forces and movement by tens of milliseconds. Expanding the applicability of neural interface technology beyond the clinical and research domains, this non-invasive spike-resolution neural interface provides the potential to augment and ultimately test the limits of human output capacity.

This talk is part of the Computational Neuroscience series.

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