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Journal articleTanzarella S, Muceli S, Del Vecchio A, et al., 2020,
Non-invasive analysis of motor neurons controlling the intrinsic and extrinsic muscles of the hand
, Journal of Neural Engineering, Vol: 17, ISSN: 1741-2552OBJECTIVE: We present a non-invasive framework for investigating efferent commands to 14 extrinsic and intrinsic hand muscles. We extend previous studies (limited to a few muscles) on common synaptic input among pools of motor neurons in a large number of muscles. APPROACH: Seven subjects performed sinusoidal isometric contractions to complete seven types of grasps, with each finger and with three combinations of fingers in opposition with the thumb. High-density surface EMG (HD-sEMG) signals (384 channels in total) recorded from the 14 muscles were decomposed into the constituent motor unit action potentials. This provided a non-invasive framework for the investigation of motor neuron discharge patterns, muscle coordination and efferent commands of the hand muscles during grasping. Moreover, during grasping tasks, it was possible to identify common neural information among pools of motor neurons innervating the investigated muscles. For this purpose, principal component analysis (PCA) was applied to the smoothed discharge rates of the decoded motor units. MAIN RESULTS: We found that the first principal component (PC1) of the ensemble of decoded motor neuron spike trains explained a variance of (53.8 ± 10.5) % and was positively correlated with force (R=0.67 ± 0.01 across all subjects and tasks). By grouping the pools of motor neurons from extrinsic or intrinsic muscles, the PC1 explained a proportion of variance of (57.3 ± 10.8) % and (57.9 ± 11.8) %, respectively, and was correlated with force with R=0.61 ± 0.13 and 0.64 ± 0.12, respectively. SIGNIFICANCE: These observations demonstrate a low dimensional control of motor neurons across multiple muscles that can be exploited for extracting control signals in neural interfacing. The proposed framework was designed for hand rehabilitation perspectives, such as post-stroke rehabilitation and hand-exoskeleton control.
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Journal articleDel Vecchio A, Holobar A, Falla D, et al., 2020,
Tutorial: Analysis of motor unit discharge characteristics from high-density surface EMG signals
, Journal of Electromyography and Kinesiology, Vol: 53, Pages: 1-14, ISSN: 1050-6411Recent work demonstrated that it is possible to identify motor unit discharge times from high-density surface EMG (HDEMG) decomposition. Since then, the number of studies that use HDEMG decomposition for motor unit investigations has increased considerably. Although HDEMG decomposition is a semi-automatic process, the analysis and interpretation of the motor unit pulse trains requires a thorough inspection of the output of the decomposition result. Here, we report guidelines to perform an accurate extraction of motor unit discharge times and interpretation of the signals. This tutorial includes a discussion of the differences between the extraction of global EMG signal features versus the identification of motor unit activity for physiological investigations followed by a comprehensive guide on how to acquire, inspect, and decompose HDEMG signals, and robust extraction of motor unit discharge characteristics.
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Journal articleSagastegui Alva PG, Muceli S, Atashzar SF, et al., 2020,
Wearable multichannel haptic device for encoding proprioception in the upper limb.
, Journal of Neural Engineering, Vol: 17, Pages: 1-11, ISSN: 1741-2552OBJECTIVE: We present the design, implementation, and evaluation of a wearable multichannel haptic system. The device is a wireless closed-loop armband driven by surface electromyography and provides sensory feedback encoding proprioception. The study is motivated by restoring proprioception information in upper limb prostheses. APPROACH: The armband comprises eight vibrotactile actuators that generate distributed patterns of mechanical waves around the limb to stimulate perception and to transfer proportional information on the arm motion. An experimental study was conducted to assess: the sensory threshold in 8 locations around the forearm, the user adaptation to the sensation provided by the device, the user performance in discriminating multiple stimulation levels, and the device performance in coding proprioception using four spatial patterns of stimulation. Eight able-bodied individuals performed reaching tasks by controlling a cursor with an EMG interface in a virtual environment. Vibrotactile patterns were tested with and without visual information on the cursor position with the addition of a random rotation of the reference control system to disturb the natural control and proprioception. RESULTS: The sensation threshold depended on the actuator position and increased over time. The maximum resolution for stimuli discrimination was four. Using this resolution, four patterns of vibrotactile activation with different spatial and magnitude properties were generated to evaluate their performance in enhancing proprioception. The optimal vibration pattern varied among the participants. When the feedback was used in closed-loop control with the EMG interface, the task success rate, completion time, execution efficiency, and average target-cursor distance improved for the optimal stimulation pattern compared to the condition without visual or haptic information on the cursor position. SIGNIFICANCE: The results indicate that the vibrotactile device enhanced the par
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Journal articleKonstantin A, Yu T, Le Carpentier E, et al., 2020,
Simulation of motor unit action potential recordings from intramuscular multichannel scanning electrodes
, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 2005-2014, ISSN: 0018-9294Multi-channel intramuscular EMG (iEMG) provides information on motor neuron behavior, muscle fiber (MF) innervation geometry and, recently, has been proposed as a means to establish a human-machine interface. Objective: to provide a reliable benchmark for computational methods applied to such recordings, we propose a simulation model for iEMG signals acquired by intramuscular multi-channel electrodes. Methods: we propose several modifications to the existing motor unit action potentials (MUAPs) simulation methods, such as farthest point sampling (FPS) for the distribution of motor unit territory centers in the muscle cross-section, accurate fiber-neuron assignment algorithm, modeling of motor neuron action potential propagation delay, and a model of multi-channel scanning electrode. Results: we provide representative applications of this model to the estimation of motor unit territories and the iEMG decomposition evaluation. Also, we extend it to a full multi-channel iEMG simulator using classic linear EMG modeling. Conclusions: altogether, the proposed models provide accurate MUAPs across the entire motor unit territories and for various electrode configurations. Significance: they can be used for the development and evaluation of mathematical methods for multi-channel iEMG processing and analysis.
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Journal articleStachaczyk M, Atashzar SF, Farina D, 2020,
Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control
, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 28, Pages: 1511-1517, ISSN: 1534-4320Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however, variations in electrode-skin contact impedance and noise remain challenging for multichannel myocontrol systems. These spatial and temporal non-stationarities negatively impact the control accuracy and therefore substantially limit the clinical viability of high-density EMG techniques. Here, we propose an adaptive algorithm for automatic artefact/noise detection and attenuation for high-density EMG control. The method infers the presence of noise in each EMG channel by spectro-temporal measures of signal similarity. These measures are then used for establishing a scoring system based on an adaptive weighting and reinforcement formulation. The method was experimentally tested as a pre-processing step for a multi-class discrimination problem of 4-digit activation. The approach was proven to enhance the discriminative information content of high-density EMG signals, as well as to attenuate non-stationary artefacts, with improvements in accuracy and robustness of the classification.
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Journal articleBarsakcioglu DY, Bracklein M, Holobar A, et al., 2020,
Control of spinal motoneurons by feedback from a non-invasive real-time interface
, IEEE Transactions on Biomedical Engineering, Vol: 68, Pages: 926-935, ISSN: 0018-9294Interfacing 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.
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Journal articleYu T, Akhmadeev K, Le Carpentier E, et al., 2020,
On-line recursive decomposition of intramuscular EMG signals using GPU-implemented bayesian filtering
, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 1806-1818, ISSN: 0018-9294Objective: Real-time intramuscular electromyography (iEMG) decomposition, which is needed in biofeedback studies and interfacing applications, is a complex procedure that involves identifying the motor neuron spike trains from a streaming iEMG recording. Methods: We have previously proposed a sequential decomposition algorithm based on a Hidden Markov Model of EMG, which used Bayesian filter to estimate unknown parameters of motor unit (MU) spike trains, as well as their action potentials (MUAPs). Here, we present a modification of this original model in order to achieve a real-time performance of the algorithm as well as a parallel computation implementation of the algorithm on Graphics Processing Unit (GPU). Specifically, the Kalman filter previously used to estimate the MUAPs, is replaced by a least-mean-square filter. Additionally, we introduce a number of heuristics that help to omit the most improbable decomposition scenarios while searching for the best solution. Then, a GPU-implementation of the proposed algorithm is presented. Results: Simulated iEMG signals containing up to 10 active MUs, as well as five experimental fine-wire iEMG signals acquired from the tibialis anterior muscle, were decomposed in real time. The accuracy of decompositions depended on the level of muscle activation, but in all cases exceeded 85%. Conclusion: The proposed method and implementation provide an accurate, real-time interface with spinal motor neurons. Significance: The presented real time implementation of the decomposition algorithm substantially broadens the domain of its application.
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Journal articleAliakbaryhosseinabadi S, Farina D, Mrachacz-Kersting N, 2020,
Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis
, JOURNAL OF NEURAL ENGINEERING, Vol: 17, ISSN: 1741-2560- Author Web Link
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- Citations: 3
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Journal articleMartinez-Valdes E, Negro F, Farina D, et al., 2020,
Divergent response of low- versus high-threshold motor units to experimental muscle pain.
, J Physiol, Vol: 598, Pages: 2093-2108KEY POINTS: The neural strategies behind the control of force during muscle pain are not well understood as previous research has been limited in assessing pain responses only during low-force contractions. Here we compared, for the first time, the behaviour of motor units recruited at low and high forces in response to pain. The results showed that motor units activated at low forces were inhibited while those recruited at higher forces increased their activity in response to pain. When analysing lower- and higher-threshold motor unit behaviour at high forces we observed differential changes in discharge rate and recruitment threshold across the motor unit pool. These adjustments allow the exertion of high forces in acutely painful conditions but could eventually lead to greater fatigue and stress of the muscle tissue. ABSTRACT: During low-force contractions, motor unit discharge rates decrease when muscle pain is induced by injecting nociceptive substances into the muscle. Despite this consistent observation, it is currently unknown how the central nervous system regulates motor unit behaviour in the presence of muscle pain at high forces. For this reason, we analysed the tibialis anterior motor unit behaviour at low and high forces. Surface EMG signals were recorded from 15 healthy participants (mean age (SD) 26 (3) years, six females) using a 64-electrode grid while performing isometric ankle dorsiflexion contractions at 20% and 70% of the maximum voluntary force (MVC). Signals were decomposed and the same motor units were tracked across painful (intramuscular hypertonic saline injection) and non-painful (baseline, isotonic saline, post-pain) contractions. At 20% MVC, discharge rates decreased significantly in the painful condition (baseline vs. pain: 12.7 (1.1) Hz to 11.5 (0.9) Hz, P < 0.001). Conversely, at 70% MVC, discharge rates increased significantly during pain (baseline vs. pain: 19.7 (2.8) Hz to 21.3 (3.5) Hz, p = 0.029) and recr
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Journal articleZuo S, Heidari H, Farina D, et al., 2020,
Miniaturized magnetic sensors for implantable magnetomyography
, Advanced Materials Technologies, Vol: 5, Pages: 1-15, ISSN: 2365-709XMagnetism‐based systems are widely utilized for sensing and imaging biological phenomena, for example, the activity of the brain and the heart. Magnetomyography (MMG) is the study of muscle function through the inquiry of the magnetic signal that a muscle generates when contracted. Within the last few decades, extensive effort has been invested to identify, characterize and quantify the magnetomyogram signals. However, it is still far from a miniaturized, sensitive, inexpensive and low‐power MMG sensor. Herein, the state‐of‐the‐art magnetic sensing technologies that have the potential to realize a low‐profile implantable MMG sensor are described. The technical challenges associated with the detection of the MMG signals, including the magnetic field of the Earth and movement artifacts are also discussed. Then, the development of efficient magnetic technologies, which enable sensing pico‐Tesla signals, is advocated to revitalize the MMG technique. To conclude, spintronic‐based magnetoresistive sensing can be an appropriate technology for miniaturized wearable and implantable MMG systems.
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Journal articleHahne JM, Wilke MA, Koppe M, et al., 2020,
Longitudinal case study of regression-based hand prosthesis control in daily life
, Frontiers in Neuroscience, Vol: 14, Pages: 1-8, ISSN: 1662-453XHand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.
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Journal articleChen C, Ma S, Sheng X, et al., 2020,
Adaptive real-time identification of motor unit discharges from non-stationary high-density surface electromyographic signals
, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 3501-3509, ISSN: 0018-9294Objective. Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. Methods. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). Main results. The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was > 80% for most conditions. From experimental EMG signals, on average, 12±2 MUSTs were identified from each electrode grid with PNR of 25.0±1.8 dB, corresponding to an estimated decomposition accuracy > 75%. Conclusion and Significance. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.
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Journal articleDimitrov H, Bull AMJ, Farina D, 2020,
Real-time interface algorithm for ankle kinematics and stiffness from electromyographic signals
, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 28, Pages: 1416-1427, ISSN: 1534-4320Shortcomings in capabilities of below-knee (transtibial) prostheses, compared to their biological counterparts, still cause medical complications and functional deficit to millions of amputees around the world. Although active (powered actuation) transtibial prostheses have the potential to bridge these gaps, the current control solutions limit their efficacy. Here we describe the development of a novel interface for two degrees-of-freedom position and stiffness control for below-knee amputees. The developed algorithm for the interface relies entirely on muscle electrical signals from the lower leg. The algorithm was tested for voluntary position and stiffness control in eight able-bodied and two transtibial amputees and for voluntary stiffness control with foot position estimation while walking in eight able-bodied and one transtibial amputee. The results of the voluntary control experiment demonstrated a promising target reaching success rate, higher for amputees compared to the able-bodied individuals (82.5% and 72.5% compared to 72.5% and 68.1% for the position and position and stiffness matching tasks respectively). Further, the algorithm could provide the means to control four stiffness levels during walking in both amputee and able-bodied individuals while providing estimates of foot kinematics (gait cycle cross-correlation >75% for the sagittal and >90% for the frontal plane and gait cycle root mean square error <7.5° in sagittal and <3° in frontal plane for able-bodied and amputee individuals across three walking speeds). The results from the two experiments demonstrate the feasibility of using this novel algorithm for online control of multiple degrees of freedom and of their stiffness in lower limb prostheses.
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Journal articleKapelner T, Sartori M, Negro F, et al., 2020,
Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.
, Scientific Reports, Vol: 10, Pages: 5834-5834, ISSN: 2045-2322We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function.
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Journal articleCasolo A, Farina D, Falla D, et al., 2020,
Strength training Increases conduction velocity of high-threshold motor units.
, Medicine and Science in Sports and Exercise, Vol: 52, Pages: 955-967, ISSN: 0195-9131PURPOSE: Motor unit conduction velocity (MUCV) represents the propagation velocity of action potentials along the muscle fibres innervated by individual motor neurons and indirectly reflects the electrophysiological properties of the sarcolemma. In this study, we investigated the effect of a 4-week strength training intervention on the peripheral properties (MUCV and motor unit action potential amplitude, RMSMU) of populations of longitudinally tracked motor units (MUs). METHODS: The adjustments exhibited by 12 individuals who participated in the training (INT) were compared with 12 controls (CON). Strength training involved ballistic (4x10) and sustained (3x10) isometric ankle dorsi flexions. Measurement sessions involved the recordings of maximal voluntary isometric force (MViF) and submaximal isometric ramp contractions, while high-density surface EMG (HDsEMG) was recorded from the tibialis anterior. HDsEMG signals were decomposed into individual MU discharge timings and MUs were tracked across the intervention. RESULTS: MViF (+14.1%, P=0.003) and average MUCV (+3.00%, P=0.028) increased in the INT group, while normalized MUs recruitment threshold (RT) decreased (-14.9%, P=0.001). The slope (rate of change) of the regression between MUCV and MUs RT increased only in the INT group (+32.6%, P=0.028), indicating a progressive greater increase in MUCV for higher-threshold MUs. The intercept (initial value) of MUCV did not change following the intervention (P=0.568). The association between RMSMU and MUs RT was not altered by the training. CONCLUSION: The increase in the rate of change in MUCV as a function of MU recruitment threshold, but not the initial value of MUCV, suggests that short-term strength training elicits specific adaptations in the electrophysiological properties of the muscle fibre membrane in high-threshold motor units.
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Journal articleFarokh Atashzar S, Tavakoli M, Farina D, et al., 2020,
Editorial: Autonomy and intelligence in neurorehabilitation robotic and prosthetic technologies
, Journal of Medical Robotics Research, Vol: 5, ISSN: 2424-9068Neurorehabilitation robotic technologies and powered assistive prosthetic devices have shown great potential for accelerating motor recovery or compensating for the lost motor functions of disabled users. The functioning of these technologies relies on a highly-interactive bidirectional flow of information and physical energy between a human user and a robotic system. Thus, key factors are integrity, intelligence and quality of the interaction loops. As a result, research in this field has focused on (a) enhancing the quality and safety of the physical interaction between disabled users and robotic systems while providing a high level of intelligence and adaptability for generating assistive and therapeutic force fields; (b) detecting the user's motor intention with high spatiotemporal resolution to provide bidirectional human-machine interfacing; (c) promoting mental engagement through designing multimodal interactive interfaces and various sensory manipulation strategies. This Special Issue has collected papers that contribute to these three research areas, highlighting the importance of different aspects in human-robot interaction loops for augmenting the performance of neurorehabilitation robotic systems and prosthetic devices.
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Journal articleDel Vecchio A, Negro F, Holobar A, et al., 2020,
Direct translation of findings in isolated animal preparations to <i>in vivo</i> human motoneuron behaviour is challenging
, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 598, Pages: 1111-1112, ISSN: 0022-3751- Author Web Link
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- Citations: 1
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Journal articleGermer CM, Del Vecchio A, Negro F, et al., 2020,
Neurophysiological correlates of force control improvement induced by sinusoidal vibrotactile stimulation
, Journal of Neural Engineering, Vol: 17, Pages: 1-14, ISSN: 1741-2552Objective. An optimal level of vibrotactile stimulation has been shown to improve sensorimotor control in healthy and diseased individuals. However, the underlying neurophysiological mechanisms behind the enhanced motor performance caused by vibrotactile stimulation are yet to be fully understood. Therefore, here we aim to evaluate the effect of a cutaneous vibration on the firing behavior of motor units in a condition of improved force steadiness. Approach. Participants performed a visuomotor task, which consisted of low-intensity isometric contractions of the first dorsal interosseous (FDI) muscle, while sinusoidal (175 Hz) vibrotactile stimuli with different intensities were applied to the index finger. High-density surface electromyogram was recorded from the FDI muscle, and a decomposition algorithm was used to extract the motor unit spike trains. Additionally, computer simulations were performed using a multiscale neuromuscular model to provide a potential explanation for the experimental findings. Main results. Experimental outcomes showed that an optimal level of vibration significantly improved force steadiness (estimated as the coefficient of variation of force). The decreased force variability was accompanied by a reduction in the variability of the smoothed cumulative spike train (as an estimation of the neural drive to the muscle), and the proportion of common inputs to the FDI motor nucleus. However, the interspike interval variability did not change significantly with the vibration. A mathematical approach, together with computer simulation results suggested that vibrotactile stimulation would reduce the variance of the common synaptic input to the motor neuron pool, thereby decreasing the low frequency fluctuations of the neural drive to the muscle and force steadiness. Significance. Our results demonstrate that the decreased variability in common input accounts for the enhancement in force control induced by vibrotactile stimulation.
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Journal articleYu T, Akhmadeev K, Le Carpentier E, et al., 2020,
Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering
, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 67, Pages: 428-440, ISSN: 0018-9294- Author Web Link
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- Citations: 9
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Journal articleAman M, Bergmeister KD, Festin C, et al., 2020,
Experimental testing of bionic peripheral nerve and muscle interfaces: animal model considerations
, Frontiers in Neuroscience, Vol: 13, Pages: 1-9, ISSN: 1662-453XIntroduction: Man-machine interfacing remains the main challenge for accurate and reliable control of bionic prostheses. Implantable electrodes in nerves and muscles may overcome some of the limitations by significantly increasing the interface's reliability and bandwidth. Before human application, experimental preclinical testing is essential to assess chronic in-vivo biocompatibility and functionality. Here, we analyze available animal models, their costs and ethical challenges in special regards to simulating a potentially life-long application in a short period of time and in non-biped animals.Methods: We performed a literature analysis following the PRISMA guidelines including all animal models used to record neural or muscular activity via implantable electrodes, evaluating animal models, group size, duration, origin of publication as well as type of interface. Furthermore, behavioral, ethical, and economic considerations of these models were analyzed. Additionally, we discuss experience and surgical approaches with rat, sheep, and primate models and an approach for international standardized testing.Results: Overall, 343 studies matched the search terms, dominantly originating from the US (55%) and Europe (34%), using mainly small animal models (rat: 40%). Electrode placement was dominantly neural (77%) compared to muscular (23%). Large animal models had a mean duration of 135 ± 87.2 days, with a mean of 5.3 ± 3.4 animals per trial. Small animal models had a mean duration of 85 ± 11.2 days, with a mean of 12.4 ± 1.7 animals.Discussion: Only 37% animal models were by definition chronic tests (>3 months) and thus potentially provide information on long-term performance. Costs for large animals were up to 45 times higher than small animals. However, costs are relatively small compared to complication costs in human long-term applications. Overall, we believe a combination of small animals for preliminary primary electrode testing a
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