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  1. Background: Myoelectric-based decoding has gained popularity in upper-limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. Methods: We extracted MU information from high-density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects’ maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. Results: We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64 %MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36 %MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52,more »MU-Neu = 6.19% MVC). Conclusion: Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.« less
    Free, publicly-accessible full text available March 30, 2023
  2. Free, publicly-accessible full text available March 1, 2023
  3. We introduce perturbative spatial frequency domain imaging (p-SFDI) for fast two-dimensional (2D) mapping of the optical properties and physiological characteristics of skin and cutaneous microcirculation using spatially modulated visible light. Compared to the traditional methods for recovering 2D maps through a pixel-by-pixel inversion, p-SFDI significantly shortens parameter retrieval time, largely avoids the random fitting errors caused by measurement noise, and enhances the image reconstruction quality. The efficacy of p-SFDI is demonstrated byin vivoimaging forearm of one healthy subject, recovering the 2D spatial distribution of cutaneous hemoglobin concentration, oxygen saturation, scattering properties, the melanin content, and the epidermal thickness over a large field of view. Furthermore, the temporal and spatial variations in physiological parameters under the forearm reactive hyperemia protocol are revealed, showing its applications in monitoring temporal and spatial dynamics.

  4. A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decodingmore »method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.« less
  5. Objective: A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. Methods: First, motor units (MUs) firing information were identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Results: Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Conclusion: Our approach provides a reliable neural decoding method for dexterous finger movements. Significance: Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.
  6. With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of individual fingers continuously and concurrently during dexterous finger flexion and extension. Specifically, motor unit (MU) discharge activity was extracted from the surface high-density electromyogram (EMG) signals recorded from the finger extensors and flexors, respectively. The MU information was separated into different groups to be associated with the flexion or extension of individual fingers and was then used to predict individual finger forces during multi-finger flexion and extension tasks. Compared with the conventional EMG amplitude-based method, our method can obtain a better force estimation performance (a higher correlation and a smaller estimation error between the predicted and the measured force) when a linear regression model was used. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.
  7. Continuous and accurate decoding of intended motions is critical for human-machine interactions. Here, we developed a novel approach for real-time continuous prediction of forces in individual fingers using parallel convolutional neural networks (CNNs). We extracted populational motor unit discharge frequency using CNNs organized in a parallel structure. The CNNs parameters were trained based on two features from high-density electromyogram (HD-EMG), namely temporal energy heatmaps and frequency spectrum maps. The populational motor unit discharge frequency was then used to continuously predict finger forces based on a linear regression model. The force prediction performance was compared with a motor unit decomposition method and the conventional EMG amplitude-based method. Our results showed that the correlation coefficient between the predicted and the recorded forces of the parallel CNN approach was on average 0.91, compared with an offline decomposition method of 0.89, an online decomposition method of 0.82, and the EMG amplitude method of 0.81. Additionally, the CNN based approach showed generalizable performance, with CNN trained on one finger applying to a different finger. The outcomes suggest that our CNN based algorithm can offer an accurate and efficient force decoding method for human-machine interactions.
  8. We present a dynamic microcirculation PIPE model for functional neuroimaging, non-neuroimaging, and coherent hemodynamics spectroscopy. The temporal evolution of the concentration and oxygen saturation of hemoglobin in tissue, comprised of the contributions from the arterioles, capillaries, and venules of microvasculature, is determined by time-resolved hemodynamic and metabolic variations in blood volume, flow velocity, and oxygen consumption with a fluid mechanics treatment. Key parameters regarding microcirculation can be assessed, including the effective blood transit times through the capillaries and the venules, and the rate constant of oxygen release from hemoglobin to tissue. The vascular autoregulation can further be quantified from the relationship between the resolved blood volume and flow velocity variations. The PIPE model shows excellent agreement with the experimental cerebral and cutaneous coherent hemodynamics spectroscopy (CHS) and fMRI-BOLD data. It further identifies the impaired cerebral autoregulation distinctively in hemodialysis patients compared to healthy subjects measured by CHS. This new dynamic microcirculation PIPE model provides a valuable tool for brain and other functional studies with hemodynamic-based techniques. It is instrumental in recovering physiological parameters from analyzing and interpreting the signals measured by hemodynamic-based neuroimaging and non-neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) in responsemore »to brain activation, physiological challenges, or physical maneuvers.

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