Prediction of imagined single-joint movements in a person with high-level tetraplegia

Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements, while SUA, multispike trains (MSP), multiunit activity, and local field potential time (LFPrms), and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20-60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints.     

Performance-driven synthesis in controller-datapath systems

This paper describes new algorithms which combine state assignment and pipelining to perform timing-driven synthesis. A cycle time requirement for a controller-datapath system must be satisfied under fixed arrival times of datapath outputs and departure times of datapath inputs. As a result, the controller's design must take into account not only cycle time of the FSM in isolation, but also input arrival time specifications and output departure time requirements. Most state assignment methods minimize area; moreover, state assignment alone may not be sufficient to eliminate all delay bottlenecks. Performance-Driven Synthesis (PDS) applies both high-level and sequential optimizations to meet a cycle time requirement: we use new don't-care assignment algorithms to minimize the delays of FSM output signals on critical paths by reducing their dependencies on late-arriving FSM primary input signals; we also use new pipelining algorithms to break critical paths which cannot be fixed by state assignment. Experimental results show that PDS improves delays with little area overhead     

Coadaptive Brain–Machine Interface via Reinforcement Learning

This paper introduces and demonstrates a novel brain–machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.      

A state-space framework for movement control to dynamic goals through brain-driven interfaces

State-space estimation is a convenient framework for the design of brain-driven interfaces, where neural activity is used to control assistive devices for individuals with severe motor deficits. Recently, state-space approaches were developed to combine goal planning and trajectory-guiding neural activity in the control of reaching movements of an assistive device to static goals. In this paper, we extend these algorithms to allow for goals that may change over the course of the reach. Performance between static and dynamic goal state equations and a standard free movement state equation is compared in simulation. Simulated trials are also used to explore the possibility of incorporating activity from parietal areas that have previously been associated with dynamic goal position. Performance is quantified using mean-square error (MSE) of trajectory estimates. We also demonstrate the use of goal estimate MSE in evaluating algorithms for the control of goal-directed movements. Finally, we propose a framework to combine sensor data and control algorithms along with neural activity and state equations, to coordinate goal-directed movements through brain-driven interfaces.     

Prediction of Distal Arm Posture in 3-D Space From Shoulder Movements for Control of Upper Limb Prostheses

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation (FES) system or a transhumeral prosthesis. Such prostheses require, at the most basic level, the control of endpoint position in three dimensions, hand orientation, and grasp. Spatiotemporal synergies exist between the proximal and distal arm joints for goal-oriented reaching movements as performed by able-bodied subjects. To fit these synergies, we utilized three-layer artificial neural networks. These networks could be used as a means for obtaining user intent information during reaching movements. We conducted reaching experiments in which subjects reached to and grasped a handle in a three-dimensional gantry. In our previous work, the three rotational angles at the shoulder were used to predict elbow flexion/extension angle during reaches on a two-dimensional plane. In this paper, we extend this model to include the two translational movements at the shoulder as inputs and an additional output of forearm pronation/supination. Counterintuitively, as the complexity of the task and the complexity of the neural network architecture increased, the performance also improved.     

仿生机械臂的小脑控制模型和仿真

提出了一种控制模拟仿生机械臂延伸运动的小脑学习模型.该模型在已知小脑结构的基础上,利用脊髓反射线路的伺服机制,将神经网络嵌入模拟哺乳动物运动的控制系统中,用以控制一个６肌肉块２关节的平面手臂,真正从生物学意义上实现了由Katayama和Kawato提出的并行分层控制的有关论述,并且证明能够迅速地学会精确轨迹控制.仿真结果表明,该小脑模型能够很好地学习不能由PDF＋F控制器所提供动态逆模型的相关内容,且具有较好的理想轨迹跟踪性能.     

Direction of arrival accuracy improvement through estimation of spatial coherence loss function in non-homogeneous environments

Performance of direction of arrival (DOA) estimation is significantly degraded in practical situations. One of the conditions that decreases its performance, is the coherent loss caused by the propagation of the wavefront through random non-homogeneous media. Most of the previous methods, such as matrix fitting method and covariance matching technique, need the multidimensional search; therefore their computations are difficult and not suitable for a real-time application. In this paper, a three-stage method based on the generalised eigenvalues utilising signal subspace eigenvectors (GEESE) and Gauss–Newton (GN) algorithms, namely 3SM/GE-GN, is presented. Simulation results show the effectiveness of the proposed method. Difference of RMSE of DOA estimation between proposed method, which is independent of the coherent loss, and other methods is more clearly visible in higher snapshots. As well, in fixed snapshot, the RMSE of the proposed method is lower than others in different SNRs. Furthermore, the computational time of the simulation is very lower than two previous works.     

High-density myoelectric pattern recognition toward improved stroke rehabilitation

Myoelectric pattern-recognition techniques have been developed to infer user's intention of performing different functional movements. Thus electromyogram (EMG) can be used as control signals of assisted devices for people with disabilities. Pattern-recognition-based myoelectric control systems have rarely been designed for stroke survivors. Aiming at developing such a system for improved stroke rehabilitation, this study assessed detection of the affected limb's movement intention using high-density surface EMG recording and pattern-recognition techniques. Surface EMG signals comprised of 89 channels were recorded from 12 hemiparetic stroke subjects while they tried to perform 20 different arm, hand, and finger/thumb movements involving the affected limb. A series of pattern-recognition algorithms were implemented to identify the intended tasks of each stroke subject. High classification accuracies (96.1% ± 4.3%) were achieved, indicating that substantial motor control information can be extracted from paretic muscles of stroke survivors. Such information may potentially facilitate improved stroke rehabilitation.     

A real-time multitarget tracking system with robust multichannel CNN-UM algorithms

This paper introduces a tightly coupled topographic sensor-processor and digital signal processor (DSP) architecture for real-time visual multitarget tracking (MTT) applications. We define real-time visual MTT as the task of tracking targets contained in an input image flow at a sampling-rate that is higher than the speed of the fastest maneuvers that the targets make. We utilize a sensor-processor based on the cellular neural network universal machine architecture that permits the offloading of the main image processing tasks from the DSP and introduces opportunities for sensor adaptation based on the tracking performance feedback from the DSP. To achieve robustness, the image processing algorithms running on the sensor borrow ideas from biological systems: the input is processed in different parallel channels (spatial, spatio-temporal and temporal) and the interaction of these channels generates the measurements for the digital tracking algorithms. These algorithms (running on the DSP) are responsible for distance calculation, state estimation, data association and track maintenance. The performance of the proposed system is studied using actual hardware for different video flows containing rapidly moving maneuvering targets.     

Tuning of a nonanalytical hierarchical control system for reachingwith FES

Point-to-point functional movements involve simultaneous shoulder and elbow joint rotations. In able-bodied subjects these movements are fully automatic, and feed-forward control ensures the synergistic activity of many muscles. Synergy between joint rotations was defined and described as a scaling between joint angular velocities (M. Popovic and D. Popovic, J. Electromyog. Kinesiol., vol. 4, p. 242-53, 1994). Similarly, subjects who can control their shoulder movements may be assisted in reaching tasks by functional electrical stimulation (FES) of elbow extensor muscles. The synergistic control paradigm can be implemented in real-time by employing a hierarchically structured production-rules method. The use of production-rules necessitates the acquisition of knowledge and the assembly of a rule-base. A nonparametric technique was designed for the identification of the rules. The identification process was divided into two phases: determination of the scaling parameters, and determination of the stimulation parameters. The scaling parameters, needed for the coordination of movements, were determined in able-bodied subjects. Those depend exclusively on the initial and target positions of the hand. The number of scalings could be reduced by dividing the workspace into 12 zones. The stimulation parameters, needed for the execution of movements, were determined in subjects with paralyzed elbow extensor muscles by identifying triplets: elbow angular velocity, elbow angular acceleration (velocity increments), and the corresponding pulse durations for various classes of movements and loads attached to the hand     

Least-square identification with error bounds for real-time signalprocessing and control

Set-membership (SM) identification, which refers to a class of algorithms using certain a priori knowledge about a parametric model to constrain the solutions to certain sets, is considered. The focus is on a class of SM-based techniques that are of particular interest in applications requiring real-time processing. The optimal bounding ellipsoid (OBE) algorithms are interpreted as a blending of the classical least-square error minimization approach with knowledge of bounds on model errors arising from SM considerations. Using this interpretation, a general framework embracing all currently used OBE algorithms is developed, and strategies for adaptation and for implementation on parallel machines are discussed. Computational complexity benefits are considered for the various algorithms. The treatment is tutorial, leaving many of the formal details to an appendix that presents an archival theoretical treatment of the key results. A second appendix gives an overview of current research in the general SM identification field     

红外场景实时仿真研究

对目标和背景组成的场景的红外特性进行了实时仿真。为实现红外场景实时仿真,以场景的红外特性数据库为基础,将机器学习算法引入到仿真之中。利用遗传算法优化权值的神经网络建立了场景温度模型,结合三维可视化显示技术,模拟出不同设定条件下的场景红外图像。仿真结果显示,采用该模型的场景温度计算值和实测结果相符,并能够满足仿真对实时性的要求。     

An approach to control laws for arm motion

This paper is concerned with the investigation of arm motion between fixed boundary conditions within a two-dimensional space. An approach to the mathematical modeling and formulation ofthe control laws which govern arm reaching motion is presented.     

EMG Pattern Analysis and Classification for a Prosthetic Arm

This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps and triceps of a below-the-humerus amputated or paralyzed person. Such signals collected from a simulated amputee are synergistically generated to produce discrete lower arm movements. The purpose of this study is to utilize these signals to control an electrically driven prosthetic or orthotic arm with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition principle seems to hold which may provide a means of decomposition of any composite motion to the six basic primitive motions, e.g., humeral rotation in and out, elbow flexion and extension, and wrist pronation and supination. Since no synergy was detected for the hand movements, different inputs have to be provided for a grip. The method described is not limited by the location of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained from the shoulder muscles. However, the presentation in this paper is limited to bicep-tricep signal classification only.     

一种基于紧急程度的自时钟开始时间公平排队分组调度算法

为了克服目前GPS (Generalized Processor Sharing)类调度算法中实时应用分组的排队时延较大且不稳定的局限性,该文提出一种新的分组排队调度算法,该调度算法在计算分组服务标签时添加了一个紧急程度函数,调整了到达分组间的竞争关系,从而可以按照实时性应用的要求来调整到达分组的转发先优级,由此显著降低了实时性应用分组的排队时延和抖动幅度。分析和仿真实验表明,与GPS类其它调度算法相比,该调度算法对于实时应用的分组能提供较低的、更稳定的排队时延保证,同时还继承了GPS类算法的公平性和排队时延有界等特性,而且对系统虚拟时间的跟踪计算更为简捷高效。     

基于国产密码算法的数控网络的认证与验证模型研究及安全评估

该文针对工业控制系统安全,提出面向数控系统(NCS)网络安全保护技术框架,选用国产密码系列算法中的SM2, SM3, SM4算法,设计并建立了数控网络(CNC)认证与验证模型(AUTH-VRF),分内外两层为数控网络提供安全防护。外层为数控网络设备间通信与传输进行安全认证实现网段隔离,内层验证通信协议完整性以确保现场设备接收运行程序的正确性与有效性;通过基于SM2, SM3, SM4算法设计和部署的外层防护装置,为分布式数控(DNC)设备与数控系统之间的通信提供身份认证与文件加密传输;同时针对工业控制网络的S7Comm工业通信协议数据,通过SM3算法验证专有工业协议数据完整性。通过网络攻击实验证明,AUTH-VRF模型可以为数控网络中工业生产数据提供有效的安全认证和资源完整性保护,为满足我国关键基础设施“国内、国外工业控制系统产品共同安全可控”和“安全技术深入工业控制系统各个层级”的需求提供了实际可行的技术参考方案。     

基于双因子认证技术的网络身份识别

本文提出了一种基于动态双因子认证技术的网络身份识别方法,用户每次登录的口令是利用系统时间和用户ID通过MD5加密算法计算得到,其通过MD5加密后生成的登录口令是随机的,这将进一步提高开放网络环境下身份识别的可靠性和安全性。同时,本文还提出网络身份识别技术可以应用到各类网站的服务器上,用来完成对用户身份的识别,以提高网络系统的安全性。     

Compressed wide spectrum sensing scheme based on BP network

This paper proposes a compressed sensing (CS) scheme to reconstruct and estimate the signals. In this scheme, the framework of CS is used to break the Nyquist sampling limit, making it possible to reconstruct and estimate signals via fewer measurements than that is required traditionally. However, the reconstruction algorithms based on CS are normally non-deterministic polynomial hard (NP-hard) in mathematics, which makes difficulties in obtaining real-time analysis-results. Therefore, a new compressed sensing scheme based on back propagation (BP) neural network is proposed under an assumption that every sub-band is the same. In this new scheme, BP neural network is added into detection process, replacing for signal reconstruction and decision-making. By doing this, heavy calculation cost in reconstruction is moved into pre-training period, which can be done before the real-time analysis, bringing about a sharp reduction in time consuming. For simplify, 1-bit quantification is taken on compressed signals. Simulations demonstrate the performance enhancement in the proposed scheme: compared with normal CS-based scheme, the proposed one presents a much shorter response time as well as a better robustness performance to noise via fewer measurements.     

Blind adaptive noncoherent multiuser detection for nonlinear modulation

Noncoherent multiuser detection for nonlinear modulation was previously studied and the idea of phase-independent noncoherent decorrelation was introduced and three post-decorrelative detectors were obtained and analyzed. However, their implementation requires the knowledge of the signature waveforms of all the users, which may be available only for centralized implementation. In this paper, we obtain a blind adaptive noncoherent decorrelative detector for nonlinear modulation that is suitable for distributed implementation with the knowledge of only the normalized signals of the desired user and the additive noise variance. This detector is based on the stochastic approximation method and does not require the overhead of any kind of "training." Two adaptive algorithms are developed, one guided by every signal in the desired user's signal set individually, and the other by the user's entire signal space. While this paper focuses on the particular problem of blind adaptive noncoherent decorrelative detection, it addresses a more general adaptation issue, namely, that of improving the convergence properties of an adaptive scheme by effectively using all the information that is known, and adapting only to the part of the desired solution that is truly unknown. Convergence is shown in the mean squared error sense for both the fixed step-size and time-varying step-size versions of the two algorithms.     

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.