脑控智能轮椅控制系统

为身体高度瘫痪的残疾人士能够自由移动,提出了一种基于脑机接口实验平台BCI2000的自发想象控制轮椅的新方法。该控制系统主要由Emotiv脑电采集装置、一台笔记本电脑、一个单片机控制器组成,对采集的信号进行时频特征分析,并利用改进的感知器算法对信号进行分类。利用提示被试者想象左右手运动的脑电信号特征,实现对轮椅的左转和右转的控制,对今后进一步研究轮椅的精确控制系统具有重要的指导意义。     

融合运动想象脑电与眼电信号的机械臂控制系统开发

脑机接口(brain computer interface, BCI)旨在通过脑电信号与外部设备通信,以实现对外部设备的控制。针对目前脑机接口系统中混合多种复杂生理电信号,并且输出控制指令较少的问题,本文提出融合运动想象(motor imagery, MI)脑电与眼电信号方法扩充控制指令的轻量级机械臂控制系统。该系统分阶段融合脑电和眼电信号两种生物信号,使用双次眼电作为任务开关,运动想象脑电信号控制机械臂运动,单次眼电控制阶段切换,实现了二分类运动想象生成多种控制指令,完成了对机械臂的连续控制。其中运动想象脑电信号使用提升小波变换(lifting wavelet transform, LWT)和共空间模式(common spatial pattern, CSP)结合的方法提取特征,并采用支持向量机(support vector machines, SVM)进行分类;眼电信号通过分析无意识眼电和有意识眼电的峰值来设置阈值进行区分。为了验证系统的可行性,设计了一项脑控机械臂自主服药实验,通过在线实验测试,被试通过使用脑电信号和眼电信号实现了机械臂控制,并完成了服药流程,有利于进一步推广脑机接口技术的实际应用。     

轮椅脑控算法研究与实验验证

文章介绍了一种基于BCI实现轮椅运动控制的新型控制方法,研究了一种便携化的脑机接口范式,搭建了适用于普通轮椅的便携化脑机轮椅控制系统;系统根据脑电信号的自身特点,选用Emotiv公司的EPOC无线便携式脑电仪采集脑电电波信号,由单片机控制,实现脑电电波数据的处理,由集成两个无刷电机的制动器执行命令,选用ZD6716V3作为无刷电机的控制器,且每个电机中,都有一个霍尔传感器,提供来自电机的速度反馈信号,以精确获取每个电机的速度参数,并将电机集成在轮椅后轮上,实现轮椅速度和方向的控制;此外,进行了基于脑电识别率的控制方式实验、基于小车的脑控实验以及基于轮椅的脑控实验;实验结果表明脑电信号的准确率可以达到83%,满足实际使用需求。     

基于自发脑电的脑机接口实验研究与设计

针对基于自发脑电信号的脑机接口研究,设计了一种科学的且易实现的运动想象实验范例,利用运动想象脑电作为BCI的控制信号。该实验方案能有效地获得可识别的具有特征性的自发脑电电位,满足脑机接口实验要求,为BCI的研究提供了一种更加自然、更加实用的控制方式。     

基于DirectShow的脑机接口图像视觉刺激器

视觉刺激器是刺激脑电信号的重要方式.为了满足想象左右手运动的脑机接口系统的特定需求,为其提供人机交互界面,采用了DirectShow、多线程和并口通信技术,在DirectShow中自定义过滤器,设计并实现了基于DirectShow的实时图像视觉刺激器.系统的开发工具是VC++,运行环境是Windows,脑机接口的数据采集仪器为Biosemi公司生产的Active One多道生理信号采集系统.最后通过脑机接口实验证明:能在满足时间精度要求的前提下,完成图像刺激的控制、并口通信等功能,为想象运动脑电的效果提供了很好的保证.     

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。     

基于双树复小波变换和GBDT的运动想象脑电识别

针对脑机接口研究中的脑电信号特征提取与分类问题,提出了一种基于双树复小波变换结合GBDT的想象左右手运动脑电识别的方法。该方法首先深入研究了双树复小波变换相比于小波包变换在脑电信号特征提取方面的优势并验证了ERD/ERS现象;实验数据采用了2003年国际脑机接口竞赛的标准数据集DataSetⅢ,然后,选取了4个典型的时间段进行实验对比,利用双树复小波变换分解与重构提取运动感知节律相关信号分量的能量均值作为特征进行GBDT分类。最后,实验取得了较好的分类准确度,验证了双树复小波变换结合GBDT的方法在脑电信号识别应用中的有效性。     

基于混合计算机接口的多旋翼飞行器3维空间目标搜索

提出一种融合半自主导航、决策与接口转换子系统实现多旋翼飞行器室内3维空间目标搜索的混合计算机接口系统．半自主导航子系统为决策子系统提供2维空间可行飞行方向并实现多旋翼飞行器3维空间半自主避障．决策子系统采用联合回归模型与谱功率法从6个电极所采集的运动想象脑电信号中提取时域与频域特征,并利用支持向量机完成分类．接口转换子系统采用连续小波变换检测眨眼时的眼电特征,并通过分析这些眼动特征实现水平与垂直方向的运动想象任务接口切换．实际的室内3维空间目标搜索实验验证了该系统具有较好的适应性与控制稳定性;相比其他方法,半自主导航子系统降低了控制难度,控制精度约提高±10 cm．     

基于运动想象脑电控制的智能家居系统

将智能家居与脑机接口(Brain Computer Interface,BCI)技术相结合,利用“意念”实现对家居的操作与控制,能够为运动障碍人士提供更友好和便利的家居生活,具有重要的社会意义.本文以左右手运动意图为例提出一种基于运动想象脑电控制的智能家居系统,对系统设计中涉及的脑电信号采集、噪声滤除预处理、特征提取和分类识别等方面进行研究,并给出系统的实现方案.     

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...     

Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect’s infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.     

Electroencephalogram-Based Control of an Electric Wheelchair

This paper presents a study on electroencephalogram (EEG)-based control of an electric wheelchair. The objective is to control the direction of an electric wheelchair using only EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs. To achieve this goal, we have developed a recursive training algorithm to generate recognition patterns from EEG signals. Our experimental results demonstrate the utility of the proposed recursive training algorithm and the viability of accomplishing direction control of an electric wheelchair by only EEG signals.     

Bio-signal-based geometric modeling application for physically disabled users

This paper discusses the challenges in achieving bio-signal-based design environments. While the main motivation of this paper was to provide a user interface for physically disabled people to express their artistic natures, a special emphasis is given on graphical user interface design where bio-signals are the single input source. Among three bio-signal sources investigated—electromyography, electrooculography and electroencephalography (EEG)—stimulus-based human–computer interaction design (EEG feature extraction method) is found to be the most promising for achieving design environments to perform complex tasks. In the proposed stimulus-based brain–computer-interaction application, the user communication with a computer is achieved by coupling intended functionalities with stimuli signals on the computer screen. Constant focus on the intended command stimulates the brain. In return, the brain releases a response signals (steady state visual evoked potential). In theory, brain’s response signals and the stimulus signals are identical. Once successfully identified, the presence of a signal pattern that is identical to the one of the alternative stimulus signals (paired with a command in a user interface) indicates the intention of a user. Since each option is associated with a unique signal pattern, multiple options can simultaneously be offered to users. The main challenge of working with stimulus signals is that the response signals are weak and they are buried inside of highly polluted EEG signals that include brain’s natural activities. In this paper, we introduce a signal processing algorithm based on Lorenz systems of differential equations for identifying the source of stimulus signals. Our experiments strongly suggest that bio-signal-based design environments to perform complex tasks, including geometric modeling can be achieved by utilizing stimulus-based signal processing methodology.     

神经皮质运动区功能定位的皮层慢电位小波分析

探讨皮质脑电中皮层慢电位小波分析用于术中神经皮质(运动区)功能定位的方法.利用离散db5小波对皮质脑电信号数据进行8层小波分解并重构各单子频带信号,提取运动事件相关皮层慢电位在运动事件发生前后的能量比(ERP指标)为特征量,并构造特定阈值进行分类,结果与相应手指弯曲运动数据比较,进行检测正确率分析.将试验采集数据分成训练和测试组,分别用于特征提取方法和分类器的设计和性能检测,进行检出正确率分析.以皮层慢电位信号的ERP指标为特征量,以1.6为阈值进行分类,其分类定位检出正确率达到84%.通过皮质(运动区)皮层慢电位的小波分析方法可以更有效地进行术中运动功能区皮质定位的特征提取和分类.     

小波包熵在脑电信号分析中的应用

为研究不同脑功能状态下脑电动态非线性特征,利用小波包变换的频率划分特性,对非平稳脑电信号进行节律提取,并计算相对小波能量,反映脑电节律间的相对能量关系。结合小波包熵分析脑电在不同大脑功能状态下的脑电复杂程度。实验结果表明,小波包分解能更精确地提取特定的脑电节律,小波包熵可以准确反映大脑活动的复杂程度。本方法也为分析其他非平稳信号提供了一种新的途径。     

Analysis of Collaborative Brain Computer Interface (BCI) Based Personalized GUI for Differently Abled

Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects were asked to choose the desired task/need from the main panel of the GUI by blinking their eyes twice. The double eye blink signals extracted by using the bio-sensor of NeuroSky’s mind wave device with portable EEG sensors are used as the command signal. Each task is designed and implemented using a Matlab tool. The developed Personalized GUI application collaborated with the EEG device accesses the user’s need. Once the system identifies the desired option through the input control signal, the appropriate algorithm is called and performed. The users can also locate the next required option within the matrix. Therefore, users can easily navigate through the GUI Model. A list of personalized music, movies, books and web URL’s are preloaded in the database. Hence, it could be suitable to assist disabled people to improve their quality of life. Analysis of variance (ANOVA) is also carried out to find out the significant signals influencing a user’s need in order to improve the motion characteristics of the brain computer interface based system.     

基于脑电(EEG)信号的“愉快-不愉快”情绪分析

脑电是目前研究脑科学的重要方法之一。情绪变化可以导致脑电信号的差异,本文运用小波分析以及EEGLAB工具箱对"愉快-不愉快"这两种情绪状态下的脑电(EEG)信号进行差异化分析,然后定量地找出不同情绪状态下的信号差别,最终实现人体情绪的可视化。该研究的方法和分析结果为研究老师、学生、海员等工作或学习压力比较大的职业群体的情绪变化提供了较好的参考价值。     

基于LABVIEW的脑电信号虚拟采集系统设计

在神经科学研究领域,对大脑的观察主要来源于对脑电信号的收集与分析;当前对脑电信号收集的方法是通过专业脑电设备将信号收集保存,再由专业软件处理;由于这类仪器非常昂贵,系统体积也比较大,软件更新快,现在只能用在科学研究上,根本无法用于有规模的实验教学,更不可能一人一机;为此,提出了一种基于LABVIEW的脑电信号虚拟采集系统设计方法,使脑电收集与分析可以广泛地应用于教学;该方法首先对脑电信号虚拟采集系统的硬件进行构造,然后以硬件构造为依据,利用AR模型功率谱估计对脑电信号进行特征提取,在特征提取过程中,对模型类型与模型系数算法以及模型最佳阶数进行分析,最后通过将二阶低通滤波器与二阶高通滤波器进行串联,形成4阶Bessel带通滤波器,实现脑电信号的滤波,并以脑电信号传输电路的设计完成脑电信号虚拟采集系统的设计;实验结果证明,所提方法可以快速地对脑电信号虚拟采集系统进行设计,并为该领域的研究发展提供支撑。