本文關(guān)鍵詞： 腦電波 信號(hào)處理 線性預(yù)測(cè)器 眨眼偽跡 教育應(yīng)用　出處：《華中師范大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái),隨著生物信息技術(shù)的高速發(fā)展,腦電波及信號(hào)處理已成為腦科學(xué)和神經(jīng)信息學(xué)的重要研究方向。腦電波作為一種微弱的生物電信號(hào),經(jīng)過(guò)腦電設(shè)備的采集,如何從攜帶噪聲的腦電信號(hào)中分離和濾除掉偽跡,并得到純凈、無(wú)噪聲污染的腦電信號(hào),是專家學(xué)者研究的重點(diǎn)。經(jīng)過(guò)偽跡去除后的腦電波,其提供的有用信息,在臨床醫(yī)學(xué)、生理學(xué)、心理學(xué)及教育方面的應(yīng)用也越來(lái)越廣泛。本文討論了腦電波及信號(hào)處理,在處理眨眼偽跡方面提出了一種徹底、真實(shí)還原腦電純凈信號(hào)的算法。并研發(fā)了一套以學(xué)生注意力提升為導(dǎo)向的測(cè)試系統(tǒng),探討腦電波在教育方面的應(yīng)用前景。在人類思考的時(shí)候,其磁場(chǎng)效應(yīng)會(huì)發(fā)生作用,從而會(huì)形成一種稱為腦電波的生物電流。本文首先介紹了腦電波的產(chǎn)生機(jī)理及研究發(fā)展歷程,并對(duì)腦電波信號(hào)處理的國(guó)內(nèi)外研究成果作了簡(jiǎn)要的歸納和闡述,接著分析了腦電波在教育方面的應(yīng)用案例,得出使用腦電波進(jìn)行注意力訓(xùn)練是一種新型有效的注意力訓(xùn)練方式。引出了腦電波信號(hào)處理及教育應(yīng)用的課題及研究意義。其次,根據(jù)作者對(duì)腦電信號(hào)處理方面的研究,對(duì)比分析了多電極腦電設(shè)備與單電極腦電設(shè)備采集數(shù)據(jù)的區(qū)別,對(duì)腦電信號(hào)處理中需要用到的數(shù)學(xué)分析模型進(jìn)行深入研究,并詳細(xì)闡述腦電信號(hào)時(shí)域分析、頻域分析和時(shí)頻分析。在此基礎(chǔ)上,重點(diǎn)研究單電極腦電信號(hào)的眨眼偽跡去除算法。通過(guò)分析現(xiàn)有的眨眼偽跡去除算法,針對(duì)已有去除單電極腦電信號(hào)眨眼偽跡的ICA與小波模型相結(jié)合算法的不足,提出了一種基于自適應(yīng)線性預(yù)測(cè)器的AR模型算法,通過(guò)對(duì)比分析和實(shí)驗(yàn),其結(jié)果證明該算法在去除眨眼偽跡和還原真實(shí)干凈的腦電信號(hào)方面,切實(shí)可行,且具有一定的優(yōu)越性。最后,本文基于該算法處理后的真實(shí)腦電信號(hào),研發(fā)了一套應(yīng)用于教育教學(xué)研究的注意力測(cè)試系統(tǒng),從需求分析、系統(tǒng)設(shè)計(jì)與主要模塊實(shí)現(xiàn)等方面做了較為詳盡的闡述,使用該系統(tǒng),進(jìn)行注意力的分組測(cè)試,取得了一系列的成果。例如采用多樣化的教學(xué)方式,通過(guò)多樣化的媒體資源展示,能提高學(xué)生注意力,取得更好的教學(xué)效果。
[Abstract]:In recent years, with the rapid development of biological information technology, brain wave and signal processing have become an important research direction of brain science and neuroinformatics. How to separate and filter artifacts from noise-carrying EEG signals and get pure, noise-free EEG signals is the focus of research by experts and scholars. The useful information provided by EEG waves after removing artifacts is in clinical medicine. Physiology, psychology, and education are also becoming more and more widely used. This paper discusses brain wave and signal processing, and proposes a new method for processing blink artifacts. An algorithm for true reduction of pure EEG signals. A set of student-oriented testing systems were developed to explore the application of brain waves in education. When humans think, the magnetic field effects work. In this paper, the mechanism of brain wave generation and its research and development are introduced, and the research results of brain wave signal processing at home and abroad are briefly summarized and expounded. Then it analyzes the application of brain wave in education, and draws a conclusion that using brain wave to train attention is a new and effective method of attention training, and leads to the subject and research significance of brain wave signal processing and educational application. According to the author's research on EEG signal processing, the differences between multi-electrode EEG equipment and single-electrode EEG equipment are compared and analyzed, and the mathematical analysis model used in EEG signal processing is deeply studied. The time domain analysis, frequency domain analysis and time frequency analysis of EEG signal are described in detail. On this basis, the blink artifact removal algorithm of single electrode EEG signal is studied. An AR model algorithm based on adaptive linear predictor is proposed to solve the problem of combining ICA with wavelet model to remove blink artifact of single electrode EEG signal. Through contrast analysis and experiment, an AR model algorithm based on adaptive linear predictor is proposed. The results show that the algorithm is feasible in removing blink artifacts and reducing real and clean EEG signals. Finally, based on the real EEG signals processed by this algorithm, A set of attention test system is developed, which is applied to educational teaching research. It is described in detail from the aspects of requirement analysis, system design and main module realization. The system is used to carry out attention grouping test. A series of achievements have been achieved, such as adopting diversified teaching methods and displaying various media resources, which can improve students' attention and achieve better teaching results.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN911.7;TP311.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉靜;;試論多媒體教學(xué)對(duì)提高學(xué)生注意力的影響[J];中國(guó)教育技術(shù)裝備;2014年07期

2 鄧超;;BrainLink:大腦解放雙手[J];創(chuàng)業(yè)家;2013年08期

3 王曉椺;石立臣;呂寶糧;;干電極腦電采集技術(shù)綜述[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2010年05期

4 趙春玲;;.NET平臺(tái)下開(kāi)發(fā)三層架構(gòu)WinForm應(yīng)用程序簡(jiǎn)介[J];信息技術(shù)與信息化;2010年04期

5 潘淑均;戴秀英;;腦電生物反饋治療中學(xué)生網(wǎng)絡(luò)成癮的效果觀察[J];寧夏醫(yī)科大學(xué)學(xué)報(bào);2010年01期

6 龍枝榮;;略述注意力潛能的訓(xùn)練方法[J];湖南科技學(xué)院學(xué)報(bào);2007年12期

7 朱洪俊;;非平穩(wěn)信號(hào)自適應(yīng)濾波的小波模型與濾波方法[J];機(jī)械工程學(xué)報(bào);2006年08期

8 王德生,李江東,王曉丹,奚寧寧;70例持續(xù)性植物狀態(tài)患者腦電和誘發(fā)電位的臨床研究[J];中國(guó)康復(fù)醫(yī)學(xué)雜志;2005年01期

9 季忠,秦樹(shù)人,彭麗玲;腦電信號(hào)的現(xiàn)代分析方法[J];重慶大學(xué)學(xué)報(bào)(自然科學(xué)版);2002年09期

10 楊竹青,李勇,胡德文;獨(dú)立成分分析方法綜述[J];自動(dòng)化學(xué)報(bào);2002年05期

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