本文關鍵詞： 腦電波 信號處理 線性預測器 眨眼偽跡 教育應用　出處：《華中師范大學》2016年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著生物信息技術的高速發(fā)展,腦電波及信號處理已成為腦科學和神經信息學的重要研究方向。腦電波作為一種微弱的生物電信號,經過腦電設備的采集,如何從攜帶噪聲的腦電信號中分離和濾除掉偽跡,并得到純凈、無噪聲污染的腦電信號,是專家學者研究的重點。經過偽跡去除后的腦電波,其提供的有用信息,在臨床醫(yī)學、生理學、心理學及教育方面的應用也越來越廣泛。本文討論了腦電波及信號處理,在處理眨眼偽跡方面提出了一種徹底、真實還原腦電純凈信號的算法。并研發(fā)了一套以學生注意力提升為導向的測試系統(tǒng),探討腦電波在教育方面的應用前景。在人類思考的時候,其磁場效應會發(fā)生作用,從而會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生機理及研究發(fā)展歷程,并對腦電波信號處理的國內外研究成果作了簡要的歸納和闡述,接著分析了腦電波在教育方面的應用案例,得出使用腦電波進行注意力訓練是一種新型有效的注意力訓練方式。引出了腦電波信號處理及教育應用的課題及研究意義。其次,根據(jù)作者對腦電信號處理方面的研究,對比分析了多電極腦電設備與單電極腦電設備采集數(shù)據(jù)的區(qū)別,對腦電信號處理中需要用到的數(shù)學分析模型進行深入研究,并詳細闡述腦電信號時域分析、頻域分析和時頻分析。在此基礎上,重點研究單電極腦電信號的眨眼偽跡去除算法。通過分析現(xiàn)有的眨眼偽跡去除算法,針對已有去除單電極腦電信號眨眼偽跡的ICA與小波模型相結合算法的不足,提出了一種基于自適應線性預測器的AR模型算法,通過對比分析和實驗,其結果證明該算法在去除眨眼偽跡和還原真實干凈的腦電信號方面,切實可行,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腦電信號,研發(fā)了一套應用于教育教學研究的注意力測試系統(tǒng),從需求分析、系統(tǒng)設計與主要模塊實現(xiàn)等方面做了較為詳盡的闡述,使用該系統(tǒng),進行注意力的分組測試,取得了一系列的成果。例如采用多樣化的教學方式,通過多樣化的媒體資源展示,能提高學生注意力,取得更好的教學效果。
[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.
【學位授予單位】：華中師范大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN911.7;TP311.52

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