Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain-computer interfaces

The primary aims of this research were to examine (1) mu and beta event-related desynchronization/synchronization (ERD/ERS) during motor imagery tasks with varying movement duration and (2) the potential impacts of movement duration on ERD/ERS patterns. Motor imagery tasks included brief and continuous imagined hand movements. During an imagery task, participants imagined an indicated movement for 1 s (i.e., brief movement imagery) or 5 s (i.e., continuous movement imagery). The results of the study support (1) that mu and beta ERD/ERS patterns are elicited during imagined hand movements and (2) that movement duration affects ERS and does not affect ERD patterns, during motor movement imagery. Additionally, brief movement imagery had a greater impact on mu and beta ERD; continuous movement imagery had a greater impact on mu and beta ERS. This research will be useful for designing future brain-computer interfaces as it provides valuable insight into the dynamics of electroencephalographic (EEG) oscillatory changes during motor imagery tasks with varying movement duration.

Relevance to industry

: Brain-computer interfaces (BCIs) have gained considerable interests by both research and industry communities who want to improve the quality of life for those who suffer from severe motor disabilities, such as amyotrophic lateral sclerosis (ALS), brainstem stroke, and cerebral palsy (CP). The results of this study should be applied to EEG-based BCI system design in order to enhance accuracy and classification performance for BCI system control.     

基于运动想象脑电的上肢康复机器人

针对脑中风偏瘫患者的康复训练,设计了一种基于运动想象脑电的上肢康复机器人系统.首先,利用肢体运动3维动画刺激患者进行运动想象并通过USB脑电放大器采集运动想象脑电信号:然后,采用小波包算法进行特征向量的提取,并通过基于马氏距离的线性判别分类器分类;最后,PC利用虚拟现实技术进行视觉反馈,同时控制康复机器人.该系统使用患...     

基于脑-机接口的无线智能机器人控制系统

提出一种基于思维脑电的无线智能机器人控制系统设计方案。该系统采用想象左右手运动时产生的脑电信号作为智能服务机器人运动的控制信号,实现对服务机器人的控制,改善瘫痪患者生活自理能力。采用基于小波包分解的方法提取特征向量,利用基于欧式距离的方法进行模式识别,进而产生机器人运动控制信号,并通过LabVIEW串口发给单片机,单片机对该信号进行红外编码后发给智能机器人,用以控制其运动方向。实验结果证明,该设计方案有利于提高脑-机接口的实用性。     

基于SSVEP直接脑控机器人方向和速度研究

直接用思维意图来控制机器人而没有大脑外周神经和肌肉的参与是人类的一个梦想,目前这一研究已成为国际前沿热点和突破点.传统的脑控机器人（Brain-controlled robot,BCR）主要控制其方向,而本文旨在探讨能够同时脑控机器人方向和速度的有效方法.采用可分类目标数多、单次识别率高且训练时间短的稳态视觉诱发电位（Steady state visual evoked potentials,SSVEP）脑机交互（Brain-computer/machine interaction,BCI/BMI）方法,为脑控机器人运动规划了向左、向右、前进和后退4个方向,设计了低速、中速和高速3级运动速度并组合了9个脑控指令;进而比较并优化了SSVEP刺激目标布局间距以及刺激目标闪烁时间,采用典型相关分析（Canonical correlation analysis,CCA）进行识别.结果表明恰当设置SSVEP刺激目标数及其布局间距和刺激目标闪烁时间,可以有效提高被试/用户直接脑控机器人的性能;优化的SSVEP刺激范式三结合适应SSVEP解码的典型相关分析,8名被试脑控机器人到达终点平均用时为2分40秒,最少用时1分29秒;同时,在脑控机器人运动过程中触碰障碍平均次数为0.88,最少碰触次数为0.本研究显示基于SSVEP的脑机交互可以作为直接脑控机器人灵活运动的一种可选方法,能够实现对机器人多个运动方向和多级速度的控制;也证实了适当增加刺激目标间距可以有效提高SSVEP-BCI脑控指令识别的正确率,说明了该脑控方法的性能与刺激被试的范式有关;再次验证了CCA算法在基于SSVEP的脑机交互中具有优良的效果.最后,为克服单一SSVEP范式存在的局限,本研究也尝试把该范式与运动想象相结合的混合范式用于脑控机器人方向和速度,并进行了初步的研究,表明可以进一步改善控制速度和提高被试舒适度.本文可望为基于SSVEP或与运动想象混合的脑机交互应用于分级或精细控制机器人方向和速度提供思路,并为直接脑控机器人技术推向实际应用打下一定的基础.     

基于Spiking神经网络的脑运动分类研究

针对大脑运动皮层群体神经元信号与运动行为关系的分析,提出一种Spiking神经网络（SNN）的分类算法。SNN的网络连接权值与突触连接的延时参数采用改进的粒子群优化方法（PSO）进行训练。仿真结果表明SNN分类效果优于群体向量法（PV）分类效果,有利于实现性能更高的用于神经康复的脑机接口系统。     

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

A brain–computer interface (BCI) provides a link between the human brain and a computer. The task of discriminating four classes (left and right hands and feet) of motor imagery movements of a simple limb-based BCI is still challenging because most imaginary movements in the motor cortex have close spatial representations. We aimed to classify binary limb movements, rather than the direction of movement within one limb. We also investigated joint time-frequency methods to improve classification accuracies. Neither of these, to our knowledge, has been investigated previously in BCI. We recorded EEG data from eleven participants, and demonstrated the classification of four classes of simple-limb motor imagery with an accuracy of 91.46% using intrinsic time-scale decomposition and 88.99% using empirical mode decomposition. In binary classifications, we achieved average accuracies of 89.90% when classifying imaginary movements of left hand versus right hand, 93.1% for left hand versus right foot, 94.00% for left hand versus left foot, 83.82% for left foot versus right foot, 97.62% for right hand versus left foot, and 95.11% for right hand versus right foot. The results show that the binary classification performance is slightly better than that of four-class classification. Our results also show that there is no significant difference in terms of spatial distribution between left and right foot motor imagery movements. There is also no difference in classification performances involving left or right foot movement. This work demonstrates that binary and four-class movements of the left and right feet and hands can be classified using recorded EEG signals of the motor cortex, and an intrinsic time-scale decomposition (ITD) feature extraction method can be used for real time brain computer interface.

     

多类运动想象脑电信号的两级特征提取方法

共同空间模式（Common spatial pattern,CSP）是运动想象脑机接口（Brain-computer interface,BCI）中常用的特征提取方法,但对多类任务的分类正确率却明显低于两类任务.通过引入堆叠降噪自动编码器（Stacked denoising autoencoders,SDA）,提出了一种多类运动想象脑电信号（Electroencephalogram,EEG）的两级特征提取方法.首先利用一对多CSP（One versus rest CSP,OVR-CSP）将脑电信号变换到使信号方差区别最大的低维空间,然后通过SDA网络提取其中可以更好表达类别属性的高层抽象特征,最后使用Softmax分类器进行分类.在对BCI竞赛IV中Data-sets 2a的4类运动想象任务进行的分类实验中,平均Kappa系数达到0.69,表明了所提出的特征提取方法的有效性和鲁棒性.     

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing (BCI) has currently added a new dimension in assistive robotics. Existing brain-computer interfaces designed for position control applications suffer from two fundamental limitations. First, most of the existing schemes employ open-loop control, and thus are unable to track positional errors, resulting in failures in taking necessary online corrective actions. There are examples of a few works dealing with closed-loop electroencephalography (EEG)-based position control. These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule, which often creates a bottleneck preventing time-efficient control. Second, the existing brain-induced position controllers are designed to generate a position response like a traditional first-order system, resulting in a large steady-state error. This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential (SSVEP) induced link-selection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors. Other than the above, the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors. Experiments undertaken reveal that the steady-state error is reduced to 0.2%. The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.      

基于ORB-SLAM的室内机器人定位和三维稠密地图构建

针对在室内机器人定位和三维稠密地图构建系统中,现有方法无法同时满足高精度定位、大范围和快速性要求的问题,应用具有跟踪、地图构建和重定位三平行线程的ORB-SLAM算法估计机器人三维位姿;然后拼接深度摄像头KINECT获得的三维稠密点云,提出空间域上的关键帧提取方法剔除冗余的视频帧;接着提出子地图法进一步减少地图构建的时间,最终提高算法的整体速度。实验结果表明,所提系统能够在大范围环境中准确定位机器人位置,在运动轨迹为50 m的大范围中,机器人的均方根误差为1.04 m,即误差为2%,同时整体速度为11帧/秒,其中定位速度达到17帧/秒,可以满足室内机器人定位和三维稠密地图构建的精度、大范围和快速性的要求。     

Modelling the movement of a two-link manipulator

An algorithm for optimizing the control signal for simple movements of a two-link manipulator with four degrees of freedom is described. Based on the typical movement and functions of upper human extremities, the manipulator (so-called anthropomorphic manipulator) is composed of two links. The motion of the links is developed by four driving motors. The mathematic model is based of the Lagrange equations of the IInd king. The minimization of the time of movement with initial limitation of accuracy is obtained and the error of the final position is minimized without changing the time-optimal criterion. The relations connected with minimalization of both quality factors are considered. At the same time, the algorithm optimizes the torque distribution between the actuators which drive each link of manipulator. As well the manipulator as its activity are modelled on the digital computer. The results of the computer simulation of the algorithm, and the modelling of the time and accuracy optimal control, are presented.     

异步BCI系统中运动想象诱发脑电特征识别

针对异步运动想象脑机交互（Brain Computer Interface,BCI）系统中空闲状态检测和不同想象任务分类的问题,在小波变换提取脑电信号特征基础上,设计了阈值判别结合支持向量机的二级分类器。由于大脑想象单侧肢体运动时,会导致同侧和对侧运动皮层脑区EEG信号在μ节律上分别出现事件相关同步和去同步,而大脑处于空闲状态时则无此现象。基于大脑活动的这一特性,提出了小波能量阈值判别法,进行空闲状态检测,径向基核函数和交叉检验的支持向量机方法,进行左、右手运动想象任务分类。结果表明该分类器最佳分类正确率达到了80.7%,且整个时间消耗仅为3.0 s,可以较好地满足异步在线运动想象BCI系统的应用。     

基于眼动辅助脑电信号的手部动作分类方法

提出一种利用小波包变换和支持向量机对手部动作的运动想象脑电信号进行分类的方法。在相关眼动辅助情况下采集想象手部动作时的C3、C4 、P3和P4通道脑电信号,用小波包变换的方法提取4种特征节律波,分别计算每种节律波能量占4种节律波能量之和的比值作为特征,然后将16维特征向量输入支持向量机分类器进行手部动作分类。对上翻、下翻、展拳、握拳4种手部动作的分类实验中平均识别率为82。3%,表明眼动辅助能有效提高运动想象脑电信号可分性。     

SLAM 问题中机器人定位误差分析与控制

移动机器人同步定位与建图问题 (Simultaneous localization and mapping, SLAM) 是机器人能否在未知环境中实现完全自主的关键问题之一. 其中, 机器人定位估计对于保持地图的一致性非常重要. 本文分析了 SLAM 问题中机器人定位误差的收敛特性. 分析表明随着机器人的运动,机器人定位误差总体上逐渐增大; 在完全未知环境中无法预测机器人定位误差的上限. 根据理论分析, 本文提出了一种控制机器人定位误差在单位距离上增长速度的算法. 该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度, 从而控制机器人定位误差的增长.     

Visual motor control of a 7 DOF robot manipulator using a fuzzy SOM network

A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.     

Motor control characteristics for circular tracking movements of human wrist

Abstract

This paper investigates motor characteristics during circular tracking movements of human wrist. Ten subjects performed a visually guided target tracking task using two degrees-of-freedom wrist movements as a tracer. Based on trajectories of the tracer and the target, three control parameters in polar coordinates were considered: R error as an evaluation of the circular movement performance, theta error of the position-control precision, and omega error of the velocity-control precision. We then examined the influence of three different speeds (0.05, 0.1, and 0.2 Hz) and visibility of the target on the three parameters to observe changes in control strategy. The theta error particularly demonstrated that subjects were more dependent on position control for the lower tracking speed of 0.05 Hz with a visible target, where the highest percentage increase of 210.6% in theta errors from the target visible to the target invisible regions was reported. However, as the target speed increases, the subjects concentrated more on the velocity of the target as a main control parameter, and a minimum percentage decrease of 9.52% in the omega error appeared from target visible to target invisible regions for 0.2 Hz. The results suggest that velocity control is more dominant during target invisible or fast tracking task.     

A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Timely identification and treatment of medical conditions could facilitate faster recovery and better health. Existing systems address this issue using custom-built sensors, which are invasive and difficult to generalize. A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data. Minimal set of features relevant to distinguish medical conditions: AMF, PVF and GDF are derived from skeletal data on sampled frames across the entire action. The AMF (angular motion features) are derived to capture the angular motion of limbs during a specific action. The relative position of joints is represented by PVF (positional variation features). GDF (global displacement features) identifies the direction of overall skeletal movement. The discriminative capability of these features is illustrated by their variance across time for different actions. The classification of medical conditions is approached in two stages. In the first stage, a low-complexity binary LSTM classifier is trained to distinguish visual medical conditions from general human actions. As part of stage 2, a multi-class LSTM classifier is trained to identify the exact medical condition from a given set of visually interpretable medical conditions. The proposed features are extracted from the 2D skeletal data of NTU RGB + D and then used to train the binary and multi-class LSTM classifiers. The binary and multi-class classifiers observed average F1 scores of 77% and 73%, respectively, while the overall system produced an average F1 score of 69% and a weighted average F1 score of 80%. The multi-class classifier is found to utilize 10 to 100 times fewer parameters than existing 2D CNN-based models while producing similar levels of accuracy.     

Application of a hybrid controller to a mobile robot

This paper presents the application of a hybrid controller to the optimization of the movement of a mobile robot. Through hybrid controller processes, the optimal angle and velocity of a robot moving in a work space was determined. More effective movement resulted from these hybrid controller processes. The experimental scenarios involved a five-versus-five soccer game and a MATLAB simulation, where the proposed system dynamically assigned the robot to the target position. The hybrid controller was able to choose a better position according to the circumstances encountered. The hybrid controller that is proposed includes a support vector machine and a fuzzy logic controller. We used the method of generalized predictive control to predict the target position, and the support vector machine to determine the optimal angle and velocity required for the mobile robot to reach the goal. First, we used the generalized predictive control to predict the target position. Then, the support vector machine is used to classify the angle that must be followed by the mobile robot to reach the goal. Next, a fuzzy logic controller is designed to determine the velocity of the left and right wheels of the mobile robot. Thus generated, the velocity was optimized according to the measures obtained by the support vector machine. Finally, based on the optimal velocity of robot, the output membership function was modified. Consequently, the proposed hybrid controller allowed the robot to reach the goal quickly and effectively.     

基于混合视线-脑机接口与共享控制的人-机器人交互系统

设计了一种基于混合视线-脑机接口与共享控制的人-机器人交互系统,以使得用户可通过视线和意念对机器人末端在2维空间进行连续的运动控制,并在避障和趋近目标的任务中获得机器智能的辅助.首先,按照用户运动意念的强度对机器人末端的运动速度大小进行等比例连续调节,以提高用户对机器人的控制感以及完成任务的参与性.然后,提出了机器人末端运动方向的一种共享控制策略,动态地融合基于视线追踪技术所得到的用户方向控制指令以及由机器人避障和趋近目标的行为设定所得到的机器人系统方向控制指令,自适应地调整机器人系统对用户的辅助力度,以减轻用户脑力负荷,提高任务完成成功率.最后,针对搭建的基于混合视线-脑机接口和共享控制的人-机器人交互平台,通过实验验证了所提系统的有效性.     

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.