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.

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters.

OBJECTIVE: Robustness to non-stationary conditions is essential to develop stable and accurate wearable neural interfaces. APPROACH: We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Liebler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (~22 ms of computational time per 100-ms batches). MAIN RESULTS: The proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80°). The rate of agreement, sensitivity, and precision were ≥ 90% in ≥ 80% of the cases in both simulated and experimentally recorded data, according to a two- source validation approach. SIGNIFICANCE: The findings demonstrate the feasibility of accurately decoding MN discharges in real-time during dynamic contractions from wearable systems mounted at the wrist and forearm. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

High Performance Wearable Ultrasound as a Human-Machine Interface for Wrist and Hand Kinematic Tracking.

OBJECTIVE: Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding. METHODS: US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task. RESULTS: In the offline analysis, the wearable US system achieved an average [Formula: see text] of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of [Formula: see text]= 0.60. In online control, the participants achieved an average 93% completion rate of the targets. CONCLUSION: When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces. SIGNIFICANCE: Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings.

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section.

OBJECTIVE: Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. METHODS: Here, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across 10 participants using concomitant sEMG decomposition as the ground truth. RESULTS: 140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 ± 10.3%. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin. CONCLUSION: The proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. SIGNIFICANCE: The proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

Non-Linearity in Motor Unit Velocity Twitch Dynamics: Implications for Ultrafast Ultrasound Source Separation.

Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.

Simultaneous and proportional control of wrist and hand degrees of freedom with kinematic prediction models from high-density EMG.

To improve intuitive control and reduce training time for active upper limb prostheses, we developed a myocontrol system for 3 degrees of freedom (DoFs) of the hand and wrist. In an offline study, we systematically investigated movement sets used to train this system, to identify the optimal compromise between training time and performance. High-density surface electromyography (HDsEMG) and optical marker motion capture were recorded concurrently from the lower arms of 8 subjects performing a series of wrist and hand movements activating DoFs individually, sequentially, and simultaneously. The root mean square (RMS) feature extracted from the EMG signal and kinematics obtained from motion capture were used to train regression and classification models to predict the kinematics of wrist movements and opening and closing of the hand, respectively. Results showed successful predictions of kinematics when training with the complete training set (r2 = 0.78 for wrist regression and recall = 0.85 for hand closing/opening classification). In further analysis, the training set was substantially reduced by removing the simultaneous movements. This led to a statistically significant, but relatively small reduction of the effectiveness of the wrist controller (r2 = 0.70, p<0.05), without changes for the hand controller (closing recall = 0.83). Reducing the training time and complexity needed to control a prosthesis with simultaneous wrist control as well as detection of intention to close the hand can lead to improved uptake of upper limb prosthetics.

Myolink: A 128-Channel, [Formula: see text], Embedded Recording System, Optimized for High-Density Surface Electromyogram Acquisition.

OBJECTIVE: We present Myolink, a portable, modular, low-noise electrophysiology amplifier optimized for high-density surface electromyogram (HD sEMG) acquisition. METHODS: Myolink consists of 4 modules. Each 10 × 8 cm module can concurrently acquire 32 unipolar electrode potentials at sampling rates of up to 8 kHz with 24-bit resolution. Modules may be stacked and operated synchronously, supporting the concurrent acquisition of up to 128 channels. A custom high-performance analog front-end provides an input-referred-noise µVRMS for a bandwidth of 23-524 Hz (tuneable by design choices), which is lower than current commercial systems. Digitized signals are processed by a custom on-board FPGA-based controller and subsequently transmitted to a PC via a medical-grade isolated USB 2.0 interface. RESULTS: The system has been tested by recording experimental HD sEMG signals, which have been subsequently decomposed into motor unit action potentials. Compared to commercially available systems, the proposed recording system led to higher-quality surface EMG acquisition, as well as higher decomposition accuracy across a wide range of forces, with the greater gain for forces ≤ 20% of the maximum voluntary contraction. SIGNIFICANCE: A portable, ultra-low-noise, HD sEMG amplifier design has been implemented and characterized. The system provides IRN performance beyond the capabilities of current state-of-the-art instrumentation and this improvement has a significant effect on HD sEMG decomposition.

Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors.

Objective. Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings.Approach. We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions.Main results. The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification.Significance.These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.

Kinematics of individual muscle units in natural contractions measuredin vivousing ultrafast ultrasound.

Objective.The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e. the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions.Approach.We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation-contraction (E-C) coupling delay.Main results.Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E-C coupling were 17.9±5.3 ms, 56.6±8.4 ms, and 3.8±3.0 ms (n= 390 across ten participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions.Significance.The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions.

Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

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.

Minimum requirements for accurate and efficient real-time on-chip spike sorting.

BACKGROUND: Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e., a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting. NEW METHOD: We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching. RESULTS: We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. COMPARISON WITH EXISTING METHODS: A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data. CONCLUSIONS: Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.

An analogue front-end model for developing neural spike sorting systems.

In spike sorting systems, front-end electronics is a crucial pre-processing step that not only has a direct impact on detection and sorting accuracy, but also on power and silicon area. In this work, a behavioural front-end model is proposed to assess the impact of the design parameters (including signal-to-noise ratio, filter type/order, bandwidth, converter resolution/rate) on subsequent spike processing. Initial validation of the model is provided by applying a test stimulus to a hardware platform and comparing the measured circuit response to the expected from the behavioural model. Our model is then used to demonstrate the effect of the Analogue Front-End (AFE) on subsequent spike processing by testing established spike detection and sorting methods on a selection of systems reported in the literature. It is revealed that although these designs have a wide variation in design parameters (and thus also circuit complexity), the ultimate impact on spike processing performance is relatively low (10-15%). This can be used to inform the design of future systems to have an efficient AFE whilst also maintaining good processing performance.

Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting.

Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation.