Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devices

We routinely generate reaching arm movements to function independently. For paralyzed users of upper extremity neural prosthetic devices, flexible, high-performance reaching algorithms will be critical to restoring quality-of-life. Previously, algorithms called real-time reach state equations (RSE) were developed to integrate the user's plan and execution-related neural activity to drive reaching movements to arbitrary targets. Preliminary validation under restricted conditions suggested that RSE might yield dramatic performance improvements. Unfortunately, real-world applications of RSE have been impeded because the RSE assumes a fixed, known arrival time. Recent animal-based prototypes attempted to break the fixed-arrival-time assumption by proposing a standard model (SM) that instead restricted the user's movements to a fixed, known set of targets. Here, we leverage general purpose filter design (GPFD) to break both of these critical restrictions, freeing the paralyzed user to make reaching movements to arbitrary target sets with various arrival times and definitive stopping. In silico validation predicts that the new approach, GPFD-RSE, outperforms the SM while offering greater flexibility. We demonstrate the GPFD-RSE against SM in the simulated control of an overactuated 3-D virtual robotic arm with a real-time inverse kinematics engine.     

Model-based neural decoding of reaching movements: a maximum likelihood approach

A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.     

Bayesian network modeling for discovering "dependent synergies" among muscles in reaching movements

The coordinated activities of muscles during reaching movements can be characterized by appropriate analysis of simultaneously-recorded surface electromyograms (sEMGs). Many recent sEMG studies have analyzed muscle synergies using statistical methods such as Independent Component Analysis, which commonly assume a small set of influences upstream of the muscles (e.g., originating from the motor cortex) produce the sEMG signals. Traditionally only the amplitude of the sEMG signal was investigated. Here, we present a fundamentally different approach and model sEMG signals after the effects of amplitude have been minimized. We develop the framework of Bayesian networks (BNs) for modeling muscle activities and for analyzing the overall muscle network structure. Instead of assuming that synergies may be independently activated, we assume that neuronal activity driving a given muscle may be conditionally dependent upon neurons driving other muscles. We call the resulting interactions between muscle activity patterns "dependent synergies". The learned BN networks were explored for the purpose of classification across subjects based on hand dominance or affliction by stroke. Network structure features were investigated as classification input features and it was determined that specific edge connection patterns of 3-node subnetworks were selectively recruited during reaching movements and were differentially recruited after stroke compared to normal control subjects. The resulting classification was robust to inter-subject and within-group variability and yielded excellent classification performance. The proposed framework extends muscle synergy analysis and provides a framework for thinking about muscle activity interactions in motor control.     

力值大小及加力时间对大鼠牙齿移动及牙根吸收的影响

目的;探讨不同力量大小和加力持续时间对大鼠牙齿移动距离及移动过程中牙根吸收程度的影响。方法：通过镍钛拉簧给SD大鼠右上颌的一磨牙分别施加10g、40g、100g大小的力,分别测量右上颌第一磨牙近中根的牙根吸收面积,牙根吸收的深度和牙齿移动的距离。结果：1,2天和12天10g组和40g组右上颌第一磨牙移动的距离显著大于100g组。2.每组中牙根吸收面积和深度均随着加力时间的增加而增加,而且力值越大吸收的程度越大。结论：牙齿移动的距离及牙根吸收的程度与力值及加力时间有密切关系,轻力在增加牙齿移动距离的同时能减少牙根吸收。     

Online classification of single EEG trials during finger movements.

Many offline studies have explored the feasibility of EEG potentials related to single limb movements for a brain-computer interface (BCI) control signal. However, only few functional online single-trial BCI systems have been reported. We investigated whether inexperienced subjects could control a BCI accurately by means of visually-cued left versus right index finger movements, performed every 2 s, after only a 20-min training period. Ten subjects tried to move a circle from the center to a target location at the left or right side of the computer screen by moving their left or right index finger. The classifier was updated after each trial using the correct class labels, enabling up-to-date feedback to the subjects throughout the training. Therefore, a separate data collection session for optimizing the classification algorithm was not needed. When the performance of the BCI was tested, the classifier was not updated. Seven of the ten subjects were able to control the BCI well. They could choose the correct target in 84%-100% of the cases, 3.5-7.7 times a minute. Their mean single trial classification rate was 80% and bit rate 10 bits/min. These results encourage the development of BCIs for paralyzed persons based on detection of single-trial movement attempts.     

Modelling the self-alignment of passive chip components during reflow soldering

In my research a 3D model was created to investigate the restoring force arising and the self-alignment occurring during reflow soldering; and simulations were performed to examine the assumptions given by the model. Besides, experiments were carried out to verify both the assumptions and the simulation predictions. Passive components with the size of 0603 (1.5 × 0.75 mm) were placed with intended misplacements and their position was measured before and after soldering. Three cases were examined: how misplacements perpendicular to the longer sides of components affects the restoring force, how parallel misplacements affect the same, and how a sidewall metallization on the component influences that. Based on the results, it is shown that the degree of restoring force is higher in the case of misplacements perpendicular to the longer side of components (x-direction) than in the case of misplacements parallel to that (y-direction). However, in the case of y-direction misplacements, the restoring force increases when sidewall metallization on the components is present.     

Quantification of 3-D field effects during 2-D microwave imaging

Two-dimensional (2-D) approaches to microwave imaging have dominated the research landscape primarily due to the moderate levels of measurement data, data-acquisition time, and computational costs required. Three-dimensional (3-D) approaches have been investigated in simulation, phantom, and animal experiments. While 3-D approaches are certainly important in terms of the potential to improve image quality, their associated costs are significant at this time. In addition, benchmarks are needed to evaluate these new generation systems as more 3-D methods begin to appear. In this paper, we present a systematic series of experiments which assess the capability of our 2-D system to image classical 3-D geometries. We demonstrate where current methods suffer from 3-D effects but also identify situations where they remain quite useful. Comparisons between reconstructions utilizing phantom measurements and simulated 3-D data are also shown to validate the results. These findings suggest that for certain biomedical applications, 2-D approaches remain quite attractive.     

Prediction of neural excitation during magnetic stimulation using passive cable models

A method for predicting neural excitation during magnetic stimulation using passive cable models has been developed. This method uses the information of the threshold capacitor voltage for magnetic stimulation coils to determine the equivalent excitation thresholds for the passive transient (PT) and passive steady-state (PSS) cable models as well as for the activating function. The threshold values for the PT, PSS models, and the activating function vary only with the pulsewidth of the stimulus for a variety of coils at different locations and orientations. Furthermore, the excitation threshold for the PSS model is also independent of axon diameter and best fitted to a simple mathematical function. By comparing the transmembrane potential of the PSS model with the corresponding threshold, the prediction of excitation during magnetic stimulation can be made. Similarly, it is also possible to predict excitation using the PT model and the activating function with the corresponding thresholds provided. By taking advantage of the weighted pulsewidth, this method can even predict the excitation for stimuli with various waveforms, greatly simplifying the determination of neural excitation for magnetic stimulation.     

Contributions of intrinsic visco-elastic torques during planar index finger and wrist movements

Human hand movements have been studied for many decades, yet the role of hand biomechanics in achieving dexterity has not been fully understood. In this paper, we investigate the contributions of the intrinsic passive viscoelastic component in the hand during the coordinated wrist and hand movements. We compare the contributions of stiffness, damping, and dynamics torques under two types of joint phase movements at two speeds. The analysis of the data collected from subject studies demonstrated that the passive visco-elastic component is dominant over dynamic coupling terms. Although the exact contributions of the three torques vary under different speeds and phasic movements, the stiffness torque was the highest (at least 47%) followed by the damping torque, while the dynamics torque was the lowest (less than 11%) in all movement scenarios. Comparisons with studies involving coordinated arm movements illustrate that dominant torques in arm and hand movements are different suggesting that neural control strategies might be distinct as well.     

Electrical manifestations of muscle fatigue during concentric and eccentric isokinetic knee flexion-extension movements

The quantification of the progression of muscle fatigue during a sustained contraction is a valuable tool in several clinical applications, ranging from the evaluation of functional impairment to the development of specific rehabilitative and training protocols. In these fields, great importance is given to isokinetic contractions. The aim of this paper was twofold: first, to propose signal processing methods for assessing the spectral changes of the surface myoelectric signal due to fatigue during isokinetic concentric and eccentric knee flexion-extension movements at a given angular velocity (60 degrees/s); second, to analyze the electrical manifestations of muscle fatigue of four thigh muscles (vastus lateralis, vastus medialis, rectus femoris, and biceps femoris) in the two contraction modalities (i.e. concentric versus eccentric). We demonstrated that, when considering concentric contractions, localized muscle fatigue can be assessed by computing the mean frequency of the frequency marginal of the time-frequency distribution derived from the surface myoelectric signal collected during each contraction cycle. Stronger nonstationarities were observed in the surface myoelectric data recorded within each cyclical movement of the studied eccentric exercise. Thus we propose the computation of the instantaneous mean frequency of the signal based on an original cross-time-frequency algorithm, which proved more sensitive than the frequency marginal in tracking the spectral changes associated with localized muscle fatigue. We derived the average fatigue pattern of the investigated muscles from experimental data recorded from a sample population consisting of twenty healthy subjects and we statistically compared the two contraction modalities. Our results showed that the electrical manifestations of muscle fatigue during concentric contractions were higher than those found during eccentric contractions, although in the latter modality the torque exerted and the mechanical work produced by the subjects were larger than those recorded during the concentric exercise. The results presented in this paper have potential clinical application and they could play an important future role in investigations of muscle behavior during dynamic, highly fatiguing contractions.     

A dynamic neural network identification of electromyography and armtrajectory relationship during complex movements

The authors propose a new approach based on dynamic recurrent neural networks (DRNN) to identify, in human, the relationship between the muscle electromyographic (EMG) activity and the arm kinematics during the drawing of the figure eight using an extended arm. After learning, the DRNN simulations showed the efficiency of the model. The authors demonstrated its generalization ability to draw unlearned movements. They developed a test of its physiological plausibility by computing the error velocity vectors when small artificial lesions in the EMG signals were created. These lesion experiments demonstrated that the DRNN has identified the preferential direction of the physiological action of the studied muscles. The network also identified neural constraints such as the covariation between geometrical and kinematics parameters of the movement. This suggests that the information of raw EMG signals is largely representative of the kinematics stored in the central motor pattern. Moreover, the DRNN approach will allow one to dissociate the feedforward command (central motor pattern) and the feedback effects from muscles, skin and joints     

Quantification of temperature rise in unipolar organic conductors during short voltage-pulse excitation using electrical testing methods

To quantify the rise in device temperature caused by Joule heating during short voltage-pulse excitation at high current densities (>10 A/cm²), the device temperatures of unipolar organic conductors were measured using electrical testing methods. For a maximum voltage amplitude of 59 V at a current density of ∼300 A/cm², temperature rose over 145 °C within a pulse duration of 5 μs in an N,N'-di(1-naphthyl)-N,N'-diphenylbenzidine (α-NPD)-based single-carrier organic conductor. This result is in reasonable agreement with numerically calculated values. These findings indicate that suppressing the effects of Joule heating by carefully adjusting pulse width, substrate and organic materials, and device configuration is important to achieve further carrier injection in the ultra-high current density region (>1 kA/cm²).     

分级康复治疗对脑卒中患者吞咽功能的影响

目的:探讨分级康复治疗对脑卒中吞咽困难患者吞咽功能的影响.方法:将108例脑卒中吞咽困难患者随机分成两组,实验组和对照组各54例,实验组根据吞咽困难不同分级采取分级康复治疗,对照组采用神经内科常规治疗,治疗4周后对比两组患者吞咽功能改善情况.结果:康复治疗4周后,实验组吞咽功能有显著改善,两组患者疗效间差异有统计学意义(P＜0.05).结论:分级康复治疗可以改善卒中后患者的吞咽功能.     

An algorithm for the estimation of the signal-to-noise ratio in surface myoelectric signals generated during cyclic movements

In many applications requiring the study of the surface myoelectric signal (SMES) acquired in dynamic conditions, it is essential to have a quantitative evaluation of the quality of the collected signals. When the activation pattern of a muscle has to be obtained by means of single- or double-threshold statistical detectors, the background noise level e (noise) of the signal is a necessary input parameter. Moreover, the detection strategy of double-threshold detectors may be properly tuned when the SNR and the duty cycle (DC) of the signal are known. The aim of this paper is to present an algorithm for the estimation of e (noise), SNR, and DC of an SMES collected during cyclic movements. The algorithm is validated on synthetic signals with statistical properties similar to those of SMES, as well as on more than 100 real signals.     

Muscle stiffness during transient and continuous movements of catmuscle: perturbation characteristics and physiological relevance

Continuous stochastic position perturbations are an attractive alternative to transient perturbations in muscle and reflex studies because they allow efficient characterization of system properties. However, the relevance of the results obtained from stochastic perturbations remains unclear because they may induce a state change in muscle properties. The authors addressed this concern by comparing the force and stiffness responses of isolated muscles of the decerebrate cat elicited by stochastic perturbations to those evoked by “step” stretches of similar amplitudes. Muscle stiffness during stochastic perturbations was found to be predominantly linear and elastic in nature for a given operating point, showing no evidence of instantaneous amplitude-dependent nonlinearities, even during large movements. In contrast, force responses evoked by step stretches were found to be mainly viscous in nature and nonlinear for larger stretches, with only a small maintained (elastic) component. Stiffness magnitude decreased with displacement amplitude for both stochastic and step perturbations. The authors' results are largely consistent with the crossbridge theory of muscle contraction, indicating that transient and continuous displacements evoke different, although functionally relevant, aspects of muscle behavior. These differences have several implications for the neural control of posture and movement, and for the design of perturbations appropriate for its study     

药物诱发白鼠肝病变过程中白鼠血清光谱的研究

本文用激光诱导荧光的方法,研究了药物诱发白鼠肝病变过程中不同阶段的白鼠的血清光谱。实验结果表明,用514．5nm作激发光源,白鼠从正常经肝纤维化到肝硬化的过程中,血清光谱的荧光峰有红移现象,而用488．0nm作激光源的光谱之间则无明显的差别。此外,研究发现CCl4溶液对白鼠肝硬化的诱发量为有效。     

Control of interaction force in constant-height contact mode atomic force microscopy

Contact mode is a versatile and widely used technique for imaging samples using the Atomic Force Microscope (AFM). When contact mode imaging is performed in constant-height mode, it enables linear and faster response but leads to uncontrolled tip-sample forces. Here, a control strategy based on magnetic actuation is proposed to achieve high-bandwidth control of the tip-sample forces in constant-height contact mode AFM. A magnetic particle attached to the AFM probe is actuated by an external solenoid and employed for force regulation. A quasi-static model has been proposed and employed to develop the control strategy. Likewise, the contact natural-frequency, which decides the limit of achievable speed, has been shown to be significantly higher relative to the free probe and to be relatively insensitive to the particle size. Subsequently, a setup is developed to validate the control strategy and demonstrate reduction of tip-sample force variation by over a factor of 12 compared to conventional constant-height mode operation. Likewise, in comparison with conventional contact mode AFM, an improvement of linearity by over a factor of 9 and improvement in response speed by a factor of 100 have been demonstrated while imaging hard samples. The system has been shown to image topography at speeds of 2.44 frames per second while regulating the interaction force. Finally, the stiffness of a sample has also been characterized using the developed system and simultaneous estimation of topography has also been demonstrated. They are shown to agree well with theoretical expectations.     

Detection of rapid-eye movements in sleep studies

One of the key features of rapid-eye movement (REM) sleep is the presence of bursts of REMs. Sleep studies routinely use REMs to classify sleep stages. Moreover, REM count or density has been used in studies involving learning and various psychiatric disorders. Most of these studies have been based on the visual identification of REMs, which is generally a very time-consuming task. This and the varying definitions of REMs across scorers have warranted the development of automatic REM detection methodologies. In this paper, we present a new detection scheme that combines many of the intrinsic properties of REMs and requires minimal parameter adjustments. In the proposed method, a single parameter can be used to control the REM detection sensitivity and specificity tradeoff. Manually scored training data are used to develop the method. We assess the performance of the method against manual scoring of individual REM events and present validation results using a separate data set. The ability of the method to discriminate fast horizontal ocular movement in REM sleep from other types of events is highlighted. A key advantage of the presented method is the minimal a priori information requirement. The results of training data (recordings from five subjects) show an overall sensitivity of 78.8% and specificity of 81.6%. The performance on the testing data (recording from five subjects different from the training data) showed overall sensitivity of 67.2% and specificity of 77.5%.     

光无源器件在军事中的应用

介绍了光无源器件在海军、探潜武器、陆军、雷达、导弹和航空航天中的应用。