A non-invasive human-machine interfacing framework for investigating dexterous control of hand muscles

The recent fast development of virtual reality and robotic assistive devices enables to augment the capabilities of able-body individuals as well as to overcome the motor missing functions of neurologically impaired or amputee individuals. To control these devices, movement intentions can be captured from biological structures involved in the process of motor planning and execution, such as the central nervous system (CNS), the peripheral nervous system (in particular the spinal motor neurons) and the musculoskeletal system. Thus, human-machine interfaces (HMI) enable to transfer neural information from the neuro-muscular system to machines. To prevent any risks due to surgical operations or tissue damage in implementing these HMIs, a non-invasive approach is proposed in this thesis. In the last five decades, surface electromyography (sEMG) has been extensively explored as a non-invasive source of neural information. EMG signals are constituted by the mixed electrical activity of several recruited motor units, the fundamental components of muscle contraction. High-density sEMG (HD-sEMG) with the use of blind source separation methods enabled to identify the discharge patterns of many of these active motor units. From these decomposed discharge patterns, the net common synaptic input (CSI) to the corresponding spinal motor neurons was quantified with cross-correlation in the time and frequency domain or with principal component analysis (PCA) on one or few muscles. It has been hypothesised that this CSI would result from the contribution of spinal descending commands sent by supra-spinal structures and afferences integrated by spinal interneurons. Another motor strategy implying the integration of descending commands at the spinal level is the one regarding the coordination of many muscles to control a large number of articular joints. This neurophysiological mechanism was investigated by measuring a single EMG amplitude per muscle, thus without the use of HD-sEMG and decomposition. In this case, the aim was to understand how the central nervous system (CNS) could control a large set of muscles actuating a vast set of combinations of degrees of freedom in a modular way. Thus, time-invariant patterns of muscle coordination, i.e. muscle synergies , were found in animals and humans from EMG amplitude of many muscles, modulated by time-varying commands to be combined to fulfil complex movements. In this thesis, for the first time, we present a non-invasive framework for human-machine interfaces based on both spinal motor neuron recruitment strategy and muscle synergistic control for unifying the understanding of these two motor control strategies and producing control signals correlated to biomechanical quantities. This implies recording both from many muscles and using HD-sEMG for each muscle. We investigated 14 muscles of the hand, 6 extrinsic and 8 intrinsic. The first two studies, (in Chapters 2 and 3, respectively) present the framework for CSI quantification by PCA and the extraction of the synergistic organisation of spinal motor neurons innervating the 14 investigated muscles. For the latter analysis, in Chapter 3, we proposed the existence of what we named as motor neuron synergies extracted with non-negative matrix factorisation (NMF) from the identified motor neurons. In these first two studies, we considered 7 subjects and 7 grip types involving differently all the four fingers in opposition with the thumb. In the first study, we found that the variance explained by the CSI among all motor neuron spike trains was (53.0 ± 10.9) % and its cross-correlation with force was 0.67 ± 0.10, remarkably high with respect to previous findings. In the second study, 4 motor neuron synergies were identified and associated with the actuation of one finger in opposition with the thumb, finding even higher correlation values with force (over 0.8) for each synergy associated with a finger during the actuation of the relative finger. In Chapter 4, we then extended the set of analysed movements in a vast repertoire of gestures and repeated the analysis of Chapter 3 by finding a different synergistic organisation during the execution of tens of tasks. We divided the contribution among extrinsic and intrinsic muscles and we found that intrinsic better enable single-finger spatial discrimination, while no difference was found in regression of joint angles by dividing the two groups of muscles. Finally, in Chapter 5 we proposed the techniques of the previous chapters for cases of impairment due both to amputation and stroke. We analysed one case of pre and post rehabilitation sessions of a trans-humeral amputee, the case of a post-stroke trans-radial amputee and three cases of acute stroke, i.e. less than one month from the stroke event. We present future perspectives (Chapter 6) aimed to design and implement a platform for both rehabilitation monitoring and myoelectric control. Thus, this thesis provides a bridge between two extensively studied motor control mechanisms, i.e. motor neuron recruitment and muscle synergies, and proposes this framework as suitable for rehabilitation monitoring and control of assistive devices.Open Acces

Neuromorphic decoding of spinal motor neuron behaviour during natural hand movements for a new generation of wearable neural interfaces

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN

Doctor of Philosophy

dissertationHigh-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss via electrical stimulation. The same device could also help restore volitional control to a prosthesis-using amputee, or sensation to a Spinal cord Injury (SCI) patient, via recordings from the still-viable peripheral nerves. The overall objective of these dissertations studies is to improve the usefulness of intrafascicular electrodes, such as the Utah Slanted Electrode Array (USEA), for neuroprosthetic devices for limb loss or spinal cord injury patients. Previous work in cat sciatic nerve has shown that stimulation through the USEA can remain viable for months after implant. However, stimulation parameters were not stable, and recordings were lost rapidly and were subject to strong contamination by myoelectrical activity from adjacent muscles. Recent research has shown that even when mobility is restored to a patient, either through prosthesis or functional electrical stimulation, difficulties in using the affected limbs arise from the lack of sensory input. In the absence of the usual proprioceptive and cutaneous inputs from the limb, planning and executing motions can be challenging and sometimes lead to the user's abandonment of prostheses. To begin to address this need, I examined the ability of USEAs in cat hindlimb nerves to activate primary sensory fibers by monitoring evoked potentials in somatosensory cortex via skull-screw electrodes. I iv also monitored evoked EMG responses, and determined that it is possible to recruit sensory or motor responses independently of one another. In the second study of this dissertation, I sought to improve the long-term stability of USEAs in the PNS by physically and electrically stabilizing and protecting the array. To demonstrate the efficacy of the stabilization and shielding technique, I examined the recording capabilities of USEA electrodes and their selectivity of muscle activation over the long term in cat sciatic nerve. In addition to long-term viability, clinically useful neuroprosthetic devices will have to be capable of interfacing with complex motor systems such as the human hand. To extend previous results of USEAs in cat hindlimb nerves and to examine selectivity when interfacing with a complex sensorimotor system, I characterized EMG and cortical somatosensory responses to acute USEA stimulation in monkey arm nerves. Then, to demonstrate the functional usefulness of stimulation through the USEA. I used multi-array, multi-electrode stimulation to generate a natural, coordinated grasp

Human motor augmentation - spinal motor neurons control of redundant degrees-of-freedom

In 1963, Stan Lee introduced a new villain to the Spiderman Universe: Dr Octopus – a human equipped with multiple robotic arms that can be controlled seamlessly in coordination with his natural limbs. Throughout the last decades, turning such fiction into real-life applications gave rise to the research field of human motor augmentation, ultimately aiming to enable humans to perform motor tasks that are sheer impossible with our natural limbs alone. While a significant process was made in designing artificial supernumerary limbs, a central problem remains: identifying adequate bodily signals that allow moving supernumerary degrees-of-freedom together with our natural ones. So far, neural activity in the brain seems to hold the greatest potential for providing all the flexibility needed to ensure such coordination between natural and supernumerary degrees-of-freedom. However, accessing neural populations in the cortical regions is accompanied by an unacceptable risk for most users. A different group of neural cells can be found in the outmost layer of the motor pathway, driving the contraction of muscles and generation of force – spinal motor neurons. The development of novel neural interfaces has made it possible to study single motor neuron activity with minimal harm to the user. This allows a direct and non-invasive window into the neural activity orchestrating human movement. In this dissertation, I investigate whether these neurons innervating our muscles could provide supernumerary control signals. The results indicate, in essence, that features extracted non-invasively from motor neuron activity have the potential to overcome current limitations in supernumerary control and thus could significantly advance human motor augmentation.Open Acces

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

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion

Transcutaneous Nerve Bundle Stimulation for Dexterous Hand Grasp Patterns: Development and Exploration of an Alternative Stimulation Method

Impairment of the hand following a neurological injury such as stroke is a major contributing factor to the loss of independence and self-sufficiency. Neuromuscular Electrical Stimulation (NMES) is a widely utilized technique to help alleviate lost muscle strength by electrically eliciting muscle contractions. However, conventional NMES applied directly over the muscle belly often faces various limitations, which prevent long-term use and efficacy. Traditional NMES techniques induce rapid muscle fatigue due to non-physiological activation of fibers resulting in a decline of muscle force. For the hand, stimulation at the skin surface typically only activates the superficial extrinsic hand muscles, leading to limited multi-joint control. To overcome these limitations, we sought to develop an alternative stimulation technique that used a high-density surface electrode array to directly target major nerve bundles at a location more proximal to the muscles. First, we designed an automated stimulation paradigm to characterize the different patterns of finger flexion elicitable via the nerve stimulation method. Randomized pairs in the electrode array were used to search for the best stimulation locations. We demonstrated that the nerve stimulation can generate a variety of single and multi-finger flexion patterns, with selective sets of nerve fiber activation and high activation redundancy. Secondly, we compared the force sustainability of the proximal nerve stimulation with conventional muscle belly stimulation. We found that, with prolonged force-matched stimulations, the proximal nerve stimulation technique can significantly delay the decline of force production over time, which allowed us to elicit sustained muscle force output. Lastly, we investigated the ability of the proximal nerve stimulation to activate both the superficial and deep extrinsic finger flexors. We obtained ultrasound images of the cross section of the flexor muscles in the forearm, and image deformation was used as a surrogate measure of muscle contraction. We found that superficial and deep muscles could be separately or concurrently activated. Overall, this work demonstrated the appealing features of our nerve stimulation method in selectively recruiting different finger flexor muscles with sustained activation. The outcomes also lay the theoretical foundation for further development of proximal nerve stimulation as an alternative approach for effective hand rehabilitation.Doctor of Philosoph

A synergy-based hand control is encoded in human motor cortical areas

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery