發(fā)布時(shí)間：2018-03-10 03:28

  本文選題：運(yùn)動(dòng)想象分類　切入點(diǎn)：認(rèn)知任務(wù)模式分類　出處：《杭州電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：認(rèn)知科學(xué)是研究人類感覺、知覺、精神狀態(tài)、大腦思維過程和信息處理過程的前沿性尖端學(xué)科，該方面的研究對揭示人腦之謎具有重要意義。認(rèn)知任務(wù)的模式分類被廣泛用于構(gòu)建腦機(jī)交互系統(tǒng)、研究人腦的工作機(jī)制和各種腦疾病的發(fā)病機(jī)理。為了探索人腦的認(rèn)知機(jī)理，本文重點(diǎn)針對兩類認(rèn)知任務(wù)-運(yùn)動(dòng)想象分類任務(wù)和駕駛疲勞狀態(tài)分類任務(wù) 基于腦電信號進(jìn)行了深入研究。 運(yùn)動(dòng)想象任務(wù)的模式分類是構(gòu)建腦機(jī)交互系統(tǒng)的重要方式之一，構(gòu)建該類系統(tǒng)的關(guān)鍵在于對執(zhí)行不同肢體運(yùn)動(dòng)想象任務(wù)時(shí)的腦電進(jìn)行特征提取，然后對提取的特征進(jìn)行分類，并把特征分類結(jié)果轉(zhuǎn)化為外部設(shè)備的控制命令。針對運(yùn)動(dòng)想象任務(wù)，本文重點(diǎn)研究了腦電信號的特征提取算法，主要做了兩方面工作，一方面研究學(xué)習(xí)了多種經(jīng)典特征提取算法，針對傳統(tǒng)公共空間模式算法中濾波器成分選擇方法的不足，提出了一種基于相關(guān)系數(shù)的新濾波器成分選擇方法；另一方面本文根據(jù)傳統(tǒng)微狀態(tài)定義提出了一種廣義微狀態(tài)概念，并基于此廣義概念提出了一種新的特征提取算法。本文使用國際BCI競賽運(yùn)動(dòng)想象數(shù)據(jù)集和實(shí)驗(yàn)室自采集的數(shù)據(jù)集驗(yàn)證了上述兩種算法的有效性。 駕駛是一項(xiàng)涉及視覺、聽覺、思維和判斷等多種認(rèn)知功能的復(fù)雜任務(wù)，如何區(qū)分長時(shí)間駕駛前后的警醒狀態(tài)和疲勞狀態(tài)是本文的另一個(gè)關(guān)注重點(diǎn)。為了達(dá)到研究目的，本文首先設(shè)計(jì)了一項(xiàng)模擬駕駛實(shí)驗(yàn)，搜集了長時(shí)間駕駛過程中的腦電數(shù)據(jù)；然后使用基于格蘭杰因果關(guān)系構(gòu)建的腦效應(yīng)網(wǎng)絡(luò)對比研究了駕駛員疲勞前后腦電信號模式的變化情況。該項(xiàng)研究發(fā)現(xiàn)了易受疲勞影響的大腦區(qū)域，并且發(fā)現(xiàn)腦效應(yīng)網(wǎng)絡(luò)的若干屬性可以作為區(qū)分警醒狀態(tài)和疲勞狀態(tài)的指標(biāo)。本文使用的研究方法一定程度上克服了當(dāng)前多數(shù)疲勞檢測算法不能衡量腦區(qū)之間的信息傳遞關(guān)系的缺點(diǎn)，研究結(jié)果對于實(shí)用疲勞檢測系統(tǒng)的電極安放位置和檢測指標(biāo)的選擇具有一定的指導(dǎo)意義。 本文針對所選的兩類認(rèn)知任務(wù)進(jìn)行了深入研究。在運(yùn)動(dòng)想象分類方面，針對傳統(tǒng)公共空間模式算法提出的改進(jìn)意見思路簡潔，行之有效；基于廣義微狀態(tài)提取的特征包含了腦電模式的空間信息，分類簡單。在疲勞狀態(tài)檢測和分類方面，基于腦效應(yīng)網(wǎng)絡(luò)展開研究，從網(wǎng)絡(luò)角度可以全面衡量大腦活躍模式的全局特性和局部特性，，使用因果關(guān)系計(jì)算的效應(yīng)連接可以在一定程度上反映不同腦區(qū)間的信息流向，方法新穎。兩類任務(wù)具有很高的科研價(jià)值和實(shí)用意義。
[Abstract]:Cognitive science is a cutting-edge discipline that studies human feelings, perceptions, mental states, brain thought processes and information processing processes. The pattern classification of cognitive tasks is widely used to construct brain-computer interaction system, to study the working mechanism of human brain and the pathogenesis of various brain diseases, in order to explore the cognitive mechanism of human brain. This paper focuses on two kinds of cognitive tasks-motor imagination classification task and driving fatigue state classification task based on EEG. The pattern classification of motion imagination task is one of the important ways to construct brain-computer interaction system. The key of constructing this kind of system is to extract the feature of EEG when performing different limb motion imagination task, and then classify the extracted feature. The result of feature classification is transformed into the control command of external equipment. Aiming at the task of motion imagination, this paper focuses on the feature extraction algorithm of EEG signal, which is mainly done in two aspects. On the one hand, several classical feature extraction algorithms are studied, and a new filter component selection method based on correlation coefficient is proposed to solve the problem of filter component selection in the traditional common space pattern algorithm. On the other hand, based on the traditional definition of micro-state, a generalized concept of micro-state is proposed. Based on the generalized concept, a new feature extraction algorithm is proposed, and the validity of the above two algorithms is verified by using the image data set of international BCI competition and the self-collected data set of laboratory. Driving is a complex task involving many cognitive functions, such as vision, hearing, thinking and judgment. How to distinguish alertness and fatigue before and after driving for a long time is another focus of this paper. In this paper, a simulated driving experiment is designed to collect EEG data during a long driving period. Then the brain effect network based on Granger causality was used to compare the changes of EEG patterns in drivers before and after fatigue. The study found regions of the brain vulnerable to fatigue. It is also found that some attributes of the brain effect network can be used as indicators to distinguish alertness from fatigue. To some extent, the research method used in this paper overcomes the fact that most current fatigue detection algorithms can not measure the information between brain regions. The drawback of the information transfer relationship, The results can be used to guide the selection of electrode placement and detection index in practical fatigue detection system. In this paper, two kinds of cognitive tasks are studied in depth. In the classification of motion imagination, the improved ideas for the traditional public space model algorithm are simple and effective. The feature based on generalized micro-state extraction includes spatial information of EEG pattern, and the classification is simple. In the aspect of fatigue state detection and classification, the research is carried out based on brain effect network. The global and local characteristics of brain activity patterns can be comprehensively measured from a network perspective, and the effect connections calculated by causality can to some extent reflect the flow of information in different brain regions. The two kinds of tasks have high scientific research value and practical significance.
【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】：TN911.7

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