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

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces could provide a promising basis for human motor augmentation by extracting potentially high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurons to effectively increase the number of degrees of freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N = 7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs during the onset of neural activity. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained by history-dependent changes in motor neuron excitability. These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect varying inputs or modulations in the neuron's intrinsic excitability.

Principles of human movement augmentation and the challenges in making it a reality.

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

Reading and Modulating Cortical ß Bursts from Motor Unit Spiking Activity.

ß Oscillations (13-30 Hz) are ubiquitous in the human motor nervous system. Yet, their origins and roles are unknown. Traditionally, ß activity has been treated as a stationary signal. However, recent studies observed that cortical ß occurs in "bursting events," which are transmitted to muscles. This short-lived nature of ß events makes it possible to study the main mechanism of ß activity found in the muscles in relation to cortical ß. Here, we assessed whether muscle ß activity mainly results from cortical projections. We ran two experiments in healthy humans of both sexes (N = 15 and N = 13, respectively) to characterize ß activity at the cortical and motor unit (MU) levels during isometric contractions of the tibialis anterior muscle. We found that ß rhythms observed at the cortical and MU levels are indeed in bursts. These bursts appeared to be time-locked and had comparable average durations (40-80 ms) and rates (approximately three to four bursts per second). To further confirm that cortical and MU ß have the same source, we used a novel operant conditioning framework to allow subjects to volitionally modulate MU ß. We showed that volitional modulation of ß activity at the MU level was possible with minimal subject learning and was paralleled by similar changes in cortical ß activity. These results support the hypothesis that MU ß mainly results from cortical projections. Moreover, they demonstrate the possibility to decode cortical ß activity from MU recordings, with a potential translation to future neural interfaces that use peripheral information to identify and modulate activity in the central nervous system.SIGNIFICANCE STATEMENT We show for the first time that ß activity in motor unit (MU) populations occurs in bursting events. These bursts observed in the output of the spinal cord appear to be time-locked and share similar characteristics of ß activity at the cortical level, such as the duration and rate at which they occur. Moreover, when subjects were exposed to a novel operant conditioning paradigm and modulated MU ß activity, cortical ß activity changed in a similar way as peripheral ß. These results provide evidence for a strong correspondence between cortical and peripheral ß activity, demonstrating the cortical origin of peripheral ß and opening the pathway for a new generation of neural interfaces.

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

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.