針對腦電控制假手的運(yùn)動想象識別及其本體感覺反饋研究
發(fā)布時間：2019-04-08 18:03
【摘要】：人手作為人類最靈巧的肢體器官之一，在人類認(rèn)識世界、改造世界過程中扮演著重要的角色。人們一旦因?yàn)榻煌ㄊ鹿�、工傷等意外情況而造成手的缺失，則會給其生活帶來極大的不便，生理和心理上也會承受巨大的痛苦。 仿生假手正是在這種情況下應(yīng)運(yùn)而生，理想的假手設(shè)計應(yīng)滿足下面兩個要求，一方面要有高效可靠的控制方式，能夠準(zhǔn)確的獲取使用者的操作意圖，，并準(zhǔn)確控制假手的動作，另一方面要能夠向人體反饋假手的位置狀態(tài)、運(yùn)動狀態(tài)及周圍環(huán)境信息等，讓使用者通過假手間接獲得感覺。 本文結(jié)合課題“針對腦電控制假手的運(yùn)動想象識別及其本體感覺反饋研究”的要求，根據(jù)運(yùn)動想象腦電信號的神經(jīng)機(jī)理，對運(yùn)動想象腦電信號的采集、預(yù)處理、特征提取和模式分類等做了探討，并將其與仿生假手的控制方法相結(jié)合，提出了腦電控制假手的系統(tǒng)組成及控制策略。更深入地，在研究了本體感覺反饋現(xiàn)象之后，提出將振動誘發(fā)的運(yùn)動幻覺作為本體感覺反饋應(yīng)用到腦電控制的假手上，通過其向使用者反饋假手的運(yùn)動感覺，實(shí)現(xiàn)仿生假手更加自然的運(yùn)動感覺反饋。本文主要完成了下列工作，并取得了部分創(chuàng)新成果： （1）對腦電控制假手的感覺反饋進(jìn)行了深入的探討，介紹了目前已應(yīng)用于假手的感覺反饋方式。結(jié)合本體感覺和肌肉神經(jīng)感知機(jī)制，闡述了經(jīng)高頻振動誘發(fā)的運(yùn)動幻覺產(chǎn)生機(jī)理，并進(jìn)行了振動狀態(tài)下的本體感覺反饋實(shí)驗(yàn)，對運(yùn)動幻覺的特性做了進(jìn)一步的分析。提出利用腕部運(yùn)動幻覺作為假手本體感覺反饋應(yīng)用到腦電控制假手系統(tǒng)中。在此基礎(chǔ)上，對基于腦電信號控制的假手及其本體感覺反饋系統(tǒng)方案做了詳細(xì)的介紹。 （2）針對使用傳統(tǒng)EMD方法對腦電信號進(jìn)行消噪時會導(dǎo)致某些IMF所包含的有用信號與噪聲一起被過濾掉情況，提出了一種基于EMD小波閾值消噪的方法，克服了傳統(tǒng)EMD去噪無法保留高頻成分中有用信息的缺陷。同時，針對腦電信號中眼電偽跡干擾的問題，提出了一種基于二階非平穩(wěn)源的盲源分離算法來消除腦電信號中混雜的眼電偽跡，信號的預(yù)處理效果明顯。 （3）提出了采用模糊熵算法對腦電信號進(jìn)行特征提取，模糊熵算法選擇指數(shù)函數(shù)作為模糊函數(shù)來度量兩個向量的相似性，避免了近似熵及樣本熵使用二值函數(shù)方法缺乏連續(xù)性、對閾值的取值敏感、容易導(dǎo)致熵值突變的問題。結(jié)合運(yùn)動想象腦電信號的ERD/ERS現(xiàn)象，提出利用C3、C4通道腦電信號的分段模糊熵差值作為特征向量，最后通過實(shí)驗(yàn)對分類效果做了評價與對比。 （4）在腦電信號模式分類方面，首先介紹了線性分類方法，接著闡述了支持向量機(jī)的基本原理，討論和分析了針對支持向量機(jī)中懲罰因子C及核函數(shù)中的參數(shù)變量最優(yōu)選取的問題，闡述了基于GA優(yōu)化的支持向量機(jī)算法原理，最后通過實(shí)驗(yàn)對算法效果進(jìn)行了對比分析與討論。
[Abstract]:As one of the most dexterous human body organs, human hands play an important role in understanding the world and transforming the world. Once people lose their hands because of accident such as traffic accident, work injury and so on, they will bring great inconvenience to their life, and they will suffer a great deal of pain both physically and psychologically. The ideal design of artificial hand should meet the following two requirements. On the one hand, there should be an efficient and reliable control mode, which can accurately obtain the user's operating intention, and accurately control the movement of the artificial hand. On the other hand, we should be able to feedback the position, motion and surrounding information of the prosthetic hand to the human body, so that the user can obtain the feeling indirectly through the artificial hand. According to the requirement of the subject "recognition of Motion Imagination of artificial hand controlled by Electroencephalogram and Research of proprioceptive feedback", according to the neural mechanism of Motor Imagination EEG, the acquisition and pretreatment of Motor Imagination EEG signal are carried out in this paper. Feature extraction and pattern classification are discussed, and combined with the control method of bionic artificial hand, the system composition and control strategy of EEG controlled artificial hand are put forward. More deeply, after studying the phenomenon of proprioceptive feedback, this paper proposes to apply the vibration-induced motor hallucination as proprioceptive feedback to the artificial hand controlled by EEG, through which the motor sensation of the prosthetic hand can be fed back to the user. Realize the bionic prosthetic hand more natural motion sensation feedback. The main contents of this paper are as follows: (1) the sensory feedback of the artificial hand controlled by EEG is deeply discussed and the sensory feedback method which has been applied to the prosthetic hand is introduced in this paper. (1) the research results are as follows: (1) the sensory feedback of the artificial hand controlled by EEG is deeply discussed. In this paper, the mechanism of motor hallucination induced by high frequency vibration is expounded based on the mechanism of proprioceptive sensation and muscular nerve perception, and the experiments of proprioceptive feedback under vibration state are carried out, and the characteristics of motor hallucination are further analyzed. This paper presents the application of wrist motor hallucination as the proprioceptive feedback of prosthetic hand to electroencephalogram (EEG) control of prosthetic hand system. On this basis, the scheme of artificial hand and its proprioceptive sensory feedback system based on EEG control is introduced in detail. (2) in view of the fact that some useful signals contained in IMF are filtered out together with noise when the traditional EMD method is used to Denoise EEG signals, a denoising method based on EMD wavelet threshold is proposed. It overcomes the defect that traditional EMD denoising can not retain useful information in high frequency components. At the same time, a blind source separation algorithm based on second-order non-stationary source is proposed to eliminate the mixed eye artifacts in EEG signals, and the preprocessing effect of the signals is obvious. This paper proposes a blind source separation algorithm based on second-order non-stationary sources to eliminate the interference of eye artifacts in EEG signals. (3) the fuzzy entropy algorithm is proposed to extract the features of EEG signals. The fuzzy entropy algorithm selects the exponential function as the fuzzy function to measure the similarity between the two vectors. The approximate entropy and sample entropy are not continuous by using the binary function method, which is sensitive to the threshold value and can easily lead to the sudden change of entropy value. Combined with the ERD/ERS phenomenon of motor imagination EEG signal, the fuzzy entropy difference of C _ 3 and C _ 4 channel EEG signals is used as the characteristic vector. Finally, the classification effect is evaluated and compared by experiments. (4) in the field of EEG pattern classification, the linear classification method is introduced firstly, and then the basic principle of support vector machine (SVM) is expounded. In this paper, the optimal selection of penalty factor C and parameter variables in kernel function of support vector machine (SVM) is discussed and analyzed. The principle of support vector machine (SVM) algorithm based on GA optimization is expounded. Finally, the experimental results of the algorithm are compared and discussed.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R318.17;TP391.4
本文編號：2454794
[Abstract]:As one of the most dexterous human body organs, human hands play an important role in understanding the world and transforming the world. Once people lose their hands because of accident such as traffic accident, work injury and so on, they will bring great inconvenience to their life, and they will suffer a great deal of pain both physically and psychologically. The ideal design of artificial hand should meet the following two requirements. On the one hand, there should be an efficient and reliable control mode, which can accurately obtain the user's operating intention, and accurately control the movement of the artificial hand. On the other hand, we should be able to feedback the position, motion and surrounding information of the prosthetic hand to the human body, so that the user can obtain the feeling indirectly through the artificial hand. According to the requirement of the subject "recognition of Motion Imagination of artificial hand controlled by Electroencephalogram and Research of proprioceptive feedback", according to the neural mechanism of Motor Imagination EEG, the acquisition and pretreatment of Motor Imagination EEG signal are carried out in this paper. Feature extraction and pattern classification are discussed, and combined with the control method of bionic artificial hand, the system composition and control strategy of EEG controlled artificial hand are put forward. More deeply, after studying the phenomenon of proprioceptive feedback, this paper proposes to apply the vibration-induced motor hallucination as proprioceptive feedback to the artificial hand controlled by EEG, through which the motor sensation of the prosthetic hand can be fed back to the user. Realize the bionic prosthetic hand more natural motion sensation feedback. The main contents of this paper are as follows: (1) the sensory feedback of the artificial hand controlled by EEG is deeply discussed and the sensory feedback method which has been applied to the prosthetic hand is introduced in this paper. (1) the research results are as follows: (1) the sensory feedback of the artificial hand controlled by EEG is deeply discussed. In this paper, the mechanism of motor hallucination induced by high frequency vibration is expounded based on the mechanism of proprioceptive sensation and muscular nerve perception, and the experiments of proprioceptive feedback under vibration state are carried out, and the characteristics of motor hallucination are further analyzed. This paper presents the application of wrist motor hallucination as the proprioceptive feedback of prosthetic hand to electroencephalogram (EEG) control of prosthetic hand system. On this basis, the scheme of artificial hand and its proprioceptive sensory feedback system based on EEG control is introduced in detail. (2) in view of the fact that some useful signals contained in IMF are filtered out together with noise when the traditional EMD method is used to Denoise EEG signals, a denoising method based on EMD wavelet threshold is proposed. It overcomes the defect that traditional EMD denoising can not retain useful information in high frequency components. At the same time, a blind source separation algorithm based on second-order non-stationary source is proposed to eliminate the mixed eye artifacts in EEG signals, and the preprocessing effect of the signals is obvious. This paper proposes a blind source separation algorithm based on second-order non-stationary sources to eliminate the interference of eye artifacts in EEG signals. (3) the fuzzy entropy algorithm is proposed to extract the features of EEG signals. The fuzzy entropy algorithm selects the exponential function as the fuzzy function to measure the similarity between the two vectors. The approximate entropy and sample entropy are not continuous by using the binary function method, which is sensitive to the threshold value and can easily lead to the sudden change of entropy value. Combined with the ERD/ERS phenomenon of motor imagination EEG signal, the fuzzy entropy difference of C _ 3 and C _ 4 channel EEG signals is used as the characteristic vector. Finally, the classification effect is evaluated and compared by experiments. (4) in the field of EEG pattern classification, the linear classification method is introduced firstly, and then the basic principle of support vector machine (SVM) is expounded. In this paper, the optimal selection of penalty factor C and parameter variables in kernel function of support vector machine (SVM) is discussed and analyzed. The principle of support vector machine (SVM) algorithm based on GA optimization is expounded. Finally, the experimental results of the algorithm are compared and discussed.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R318.17;TP391.4
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