Control of Spinal Motoneurons by Feedback From a Non-Invasive Real-Time Interface

Abstract

Interfacing with human neural cells during natural tasks provides the means for investigating the working principles of the central nervous system and for developing human-machine interaction technologies. Here we present a computationally efficient non-invasive, real-time interface based on the decoding of the activity of spinal motoneurons from wearable high-density electromyogram (EMG) sensors. We validate this interface by comparing its decoding results with those obtained with invasive EMG sensors and offline decoding, as reference. Moreover, we test the interface in a series of studies involving real-time feedback on the behavior of a relatively large number of decoded motoneurons. The results on accuracy, intuitiveness, and stability of control demonstrate the possibility of establishing a direct non-invasive interface with the human spinal cord without the need for extensive training. Moreover, in a control task, we show that the accuracy in control of the proposed neural interface may approach that of the natural control of force. These results are the first that demonstrate the feasibility and validity of a non-invasive direct neural interface with the spinal cord, with wearable systems and matching the neural information flow of natural movements.

Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array

“…The surface EMG signals were decomposed into constituent motor unit action potentials (MUAPs) by blind source separation in offline (18, 30) and online (31) implementations. We used a convolution kernel compensation technique that has been described previously (17) and validated in synthetic datasets (18, 30), intramuscular EMG motor unit recordings (32, 33), and during a wide-range of voluntary force contractions in both healthy humans (34, 35) Parkinson’s disease (36) and after targeted muscle reinnervation (37, 38).…”

Non-invasive real-time access to the output of the spinal cord via a wrist wearable interface

“…To extract motoneurons' firings in real-time, we implemented a dual phase blind source separation (23). In the first calibrating phase, the algorithm followed the same procedure described above to identify the latent motoneurons.…”

“…In the first calibrating phase, the algorithm followed the same procedure described above to identify the latent motoneurons. Then, the obtained inverse of the end-offiber potential filters was applied to new tendon electric signals epochs to decompose the activity of the previously identified motoneurons, and detect new action potentials using the stored spike and noise centroids of each source (23). During calibration, motoneurons with more than 20% of shared spikes were considered equal and only the one with highest pulse-to-noise ratio was preserved to avoid redundant activity.…”

“…This approach has been developed for muscle recordings but not for tendon potentials. For muscle signals, the inverse problem is often solved by blind source separation (19)(20)(21)(22)(23). The sparseness of the estimated sources is maximized under the assumption that the action potentials of the muscle fibers innervated by each motoneuron are unique with respect to those elicited by other motoneurons.…”

The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

“…Further, it addressed how this potential ability depends on the similarity of the MU pairs in size or, equivalently, in recruitment threshold. For this purpose, we used a neural interface that provided subjects with biofeedback on the activity of individual MUs [25]. Subjects were encouraged to navigate a cursor inside a 2D space into different targets as quickly as possible by selectively recruiting different MUs.…”