混合脑机接口在康复机器人上的应用
发布时间：2018-11-05 10:46
【摘要】：脑机接口(Brain-computer interface,BCI)通过借助计算机或其他外部电子设备,旨在建立一种不依赖人体神经和肌肉组织等正常传输通道,而直接进行人脑与外界之间信息交流的新途径。在助残康复、智能生活、娱乐等领域有着广泛的应用前景。本文从实现对康复训练机器人的控制来进行康复训练出发,以运动想象和P300脑电信号为切入点,并结合他们各自的优势构建混合BCI系统。本文主要做了以下工作:(1)经典的共同空间模式(Common spatial pattern,CSP)用于两类运动想象的特征提取,通过对CSP进行扩展将其用于多类问题上。本文首先对多类CSP方法一对多CSP(One versus rest CSP,OVR-CSP)进行了研究。由于OVR-CSP滤波器的性能依赖于其选择的频带,当在不合适的频率段进行滤波的特征上执行分类时,其分类精度一般很差。在此基础上进一步的研究了对频带进行固定划分的Filter bank共同空间模式方法,通过频带的划分虽然能够进一步提高分类正确率,但却还是远低于两类问题。(2)针对常用多类CSP算法在BCI信号处理方面存在识别率较低的问题,通过引入堆叠降噪自动编码器(Stacked denoising autoencoders,SDA),提出了一种多类变频带运动想象脑电信号的两级特征提取方法。首先将原始信号通过变化频率段带通滤波器得到不同频段的信号,其次利用OVR-CSP将不同频段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,接着将获得的特征使用Relief F方法进行特征选择,选择出最大权值所对应频带的特征,最后使用Softmax分类器进行分类。在对BCI竞赛IV中Datasets 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0.70,表明了所提出的特征提取方法的有效性和鲁棒性。(3)通过对现有P300范式的研究,提出了一种基于变概率的刺激范式(Variable probability paradigm,VPP)。在该范式中,字符呈现不均匀分布,其密度从中间向两边依次减小。字符识别分为两步进行,先进行随机行闪烁确定字符所在行,然后所选行中的字符再进行随机闪烁以确定目标字符。使用该范式和基于区域的范式进行数据采集及处理,结果表明VPP的信息传输率比基于区域的范式提高约10%,证明了该范式的可行性。(4)为了实现对康复机器人的多维控制,本文设计了一种基于运动想象(Motor imagery,MI)和P300信号的混合BCI控制策略。使用P300信号作为两种信号间切换的“开关”,选择以游戏图标组成的VPP作为游戏菜单的控制面板,MI作为机器人的控制信号来实现患者康复训练。通过离线数据采集实验进行模拟控制,结果表明了该系统的可行性。
[Abstract]:The brain-computer interface (Brain-computer interface,BCI) aims to establish a new way to directly communicate information between human brain and the outside world by means of computer or other external electronic devices, which is independent of normal transmission channels such as human nerve and muscle tissue. In the disability rehabilitation, intelligent life, entertainment and other fields have a wide range of applications. In order to control the rehabilitation training robot, this paper starts from the exercise imagination and P300 EEG signal as the starting point, and combines their respective advantages to construct a hybrid BCI system. The main work of this paper is as follows: (1) the classical common space model (Common spatial pattern,CSP) is used to extract the feature of two kinds of motion imagination, and the CSP is extended to multi-class problems. In this paper, one-to-many CSP (One versus rest CSP,OVR-CSP of multi-class CSP methods are studied. Because the performance of OVR-CSP filter depends on its selected frequency band, the classification accuracy is generally very poor when the classification is performed on the characteristics of the filter in an inappropriate frequency band. On this basis, we further study the Filter bank common space pattern method for the fixed division of the frequency band. Though the classification of the frequency band can further improve the classification accuracy, But it is still far lower than two kinds of problems. (2) aiming at the problem of low recognition rate in BCI signal processing of common multi-class CSP algorithm, the stack noise reduction automatic encoder (Stacked denoising autoencoders,SDA is introduced. A two-stage feature extraction method for multi-frequency band motion imagination EEG signals is proposed. First, the original signal is obtained by the bandpass filter in the variable frequency band, and then the signal in different frequency bands is transformed into a low-dimensional space in which the variance of the signal is the most different by using OVR-CSP. Then the high-level abstract features which can better express the category attributes are extracted by SDA network, and then the features obtained are selected by Relief F method, and the features corresponding to the frequency band corresponding to the maximum weights are selected. Finally, Softmax classifier is used to classify. In the classification experiment of four kinds of motion imagination tasks of Datasets 2a in BCI competition IV, the average Kappa coefficient is 0.70, which indicates the effectiveness and robustness of the proposed feature extraction method. (3) by studying the existing P300 normal form, In this paper, we propose a variable probabilistic stimulus normal form (Variable probability paradigm,VPP). In this paradigm, characters are distributed unevenly, and their density decreases from middle to both sides. Character recognition is divided into two steps: the random line flashes to determine the line of the character, and then the character in the selected line is flashed randomly to determine the target character. The results show that the information transfer rate of VPP is about 10% higher than that of region based paradigm. The feasibility of this paradigm is proved. (4) in order to realize multidimensional control of rehabilitation robot, a hybrid BCI control strategy based on motion imagination (Motor imagery,MI) and P300 signal is designed in this paper. The P300 signal is used as the switch between the two signals. The VPP composed of game icons is chosen as the control panel of the game menu and MI is used as the control signal of the robot to realize the rehabilitation training of patients. The simulation control is carried out by off-line data acquisition experiment, and the results show that the system is feasible.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242
[Abstract]:The brain-computer interface (Brain-computer interface,BCI) aims to establish a new way to directly communicate information between human brain and the outside world by means of computer or other external electronic devices, which is independent of normal transmission channels such as human nerve and muscle tissue. In the disability rehabilitation, intelligent life, entertainment and other fields have a wide range of applications. In order to control the rehabilitation training robot, this paper starts from the exercise imagination and P300 EEG signal as the starting point, and combines their respective advantages to construct a hybrid BCI system. The main work of this paper is as follows: (1) the classical common space model (Common spatial pattern,CSP) is used to extract the feature of two kinds of motion imagination, and the CSP is extended to multi-class problems. In this paper, one-to-many CSP (One versus rest CSP,OVR-CSP of multi-class CSP methods are studied. Because the performance of OVR-CSP filter depends on its selected frequency band, the classification accuracy is generally very poor when the classification is performed on the characteristics of the filter in an inappropriate frequency band. On this basis, we further study the Filter bank common space pattern method for the fixed division of the frequency band. Though the classification of the frequency band can further improve the classification accuracy, But it is still far lower than two kinds of problems. (2) aiming at the problem of low recognition rate in BCI signal processing of common multi-class CSP algorithm, the stack noise reduction automatic encoder (Stacked denoising autoencoders,SDA is introduced. A two-stage feature extraction method for multi-frequency band motion imagination EEG signals is proposed. First, the original signal is obtained by the bandpass filter in the variable frequency band, and then the signal in different frequency bands is transformed into a low-dimensional space in which the variance of the signal is the most different by using OVR-CSP. Then the high-level abstract features which can better express the category attributes are extracted by SDA network, and then the features obtained are selected by Relief F method, and the features corresponding to the frequency band corresponding to the maximum weights are selected. Finally, Softmax classifier is used to classify. In the classification experiment of four kinds of motion imagination tasks of Datasets 2a in BCI competition IV, the average Kappa coefficient is 0.70, which indicates the effectiveness and robustness of the proposed feature extraction method. (3) by studying the existing P300 normal form, In this paper, we propose a variable probabilistic stimulus normal form (Variable probability paradigm,VPP). In this paradigm, characters are distributed unevenly, and their density decreases from middle to both sides. Character recognition is divided into two steps: the random line flashes to determine the line of the character, and then the character in the selected line is flashed randomly to determine the target character. The results show that the information transfer rate of VPP is about 10% higher than that of region based paradigm. The feasibility of this paradigm is proved. (4) in order to realize multidimensional control of rehabilitation robot, a hybrid BCI control strategy based on motion imagination (Motor imagery,MI) and P300 signal is designed in this paper. The P300 signal is used as the switch between the two signals. The VPP composed of game icons is chosen as the control panel of the game menu and MI is used as the control signal of the robot to realize the rehabilitation training of patients. The simulation control is carried out by off-line data acquisition experiment, and the results show that the system is feasible.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242
【参考文献】
相关期刊论文 前10条
1 汪彩霞;魏雪云;王彪;;基于堆栈降噪自动编码模型的动态纹理分类方法[J];现代电子技术;2015年06期
2 马玉良;许明珍;佘青山;高云园;孙曜;杨家强;;基于自适应阈值的脑电信号去噪方法[J];传感技术学报;2014年10期
3 徐守晶;韩立新;曾晓勤;;基于改进型SDA的自然图像分类与检索[J];模式识别与人工智能;2014年08期
4 刘斌;魏梦然;罗聪;;基于脑电BCI的研究综述[J];电脑知识与技术;2014年07期
5 刘冲;颜世玉;赵海滨;王宏;;多类运动想象任务脑电信号的KNN分类研究[J];仪器仪表学报;2012年08期
6 刘广权;黄淦;朱向阳;;共空域模式方法在多类别分类中的应用[J];中国生物医学工程学报;2009年06期
7 李明爱;刘净瑜;郝冬梅;;基于改进CSP算法的运动想象脑电信号识别方法[J];中国生物医学工程学报;2009年02期
8 马，
本文编号：2311821
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2311821.html
