【摘要】：注意力是心理學(xué)的一個(gè)概念,同時(shí)也是腦科學(xué)和生物信息學(xué)中的一個(gè)研究對(duì)象。監(jiān)測(cè)人的注意力可以成為人們學(xué)習(xí)或者工作的一種輔助工具,進(jìn)而使人的學(xué)習(xí)和工作效率得到提高,甚至少犯許多嚴(yán)重的錯(cuò)誤,例如在學(xué)習(xí)的過程中,如果有一個(gè)監(jiān)測(cè)學(xué)生注意力的工具,那么老師可以在學(xué)生注意力不集中的時(shí)候提醒學(xué)生,學(xué)生就可以及時(shí)更正自己的精神狀態(tài)。結(jié)合計(jì)算機(jī)科學(xué)及生物信息科學(xué),我們可以通過腦機(jī)接口來(lái)監(jiān)測(cè)人的注意力。 用計(jì)算機(jī)開發(fā)各種用途的腦機(jī)接口是當(dāng)前的一個(gè)研究熱點(diǎn),但是,目前絕大部分腦機(jī)接口的類型都是動(dòng)作類型,即通過腦機(jī)接口來(lái)監(jiān)測(cè)大腦在想象做什么樣的動(dòng)作,進(jìn)而由腦機(jī)接口向外部工作發(fā)出相同動(dòng)作的指令,這種腦機(jī)接口的典型應(yīng)用就是機(jī)械臂。而關(guān)于實(shí)時(shí)監(jiān)測(cè)注意力這樣的精神狀態(tài)的腦機(jī)接口研究比較欠缺,因?yàn)樽⒁饬ι婕暗皆S多心理知識(shí),研究過程比較復(fù)雜。而開發(fā)一個(gè)實(shí)時(shí)的監(jiān)測(cè)注意力的應(yīng)用系統(tǒng)更加少,更多的是離線處理的腦機(jī)接口,因?yàn)槟X電波的分析處理十分復(fù)雜,中間的每一處理步驟都需要消耗一定的時(shí)間,很難做到實(shí)時(shí)監(jiān)測(cè)。 本論文對(duì)當(dāng)前的腦機(jī)接口的技術(shù)和基礎(chǔ)理論進(jìn)行了深入的分析,并吸收了諸多腦機(jī)接口開發(fā)的經(jīng)驗(yàn)和理論知識(shí),自主開發(fā)了一個(gè)實(shí)時(shí)監(jiān)測(cè)的腦機(jī)接口,并將其應(yīng)用于監(jiān)測(cè)人在學(xué)習(xí)中的注意力。該腦機(jī)接口采用小波變換對(duì)腦電信號(hào)進(jìn)行去噪,然后用基于漢寧窗的FIR算法提取了6個(gè)特征值,最后用KNN (k-Nearest Neighbor)算法進(jìn)行分類。腦機(jī)接口需要經(jīng)過大量的訓(xùn)練才能應(yīng)用,通常一個(gè)腦機(jī)接口從一開始到最終的應(yīng)用需要經(jīng)過幾個(gè)月的訓(xùn)練,因此,分類過程將隨著訓(xùn)練數(shù)據(jù)的增多而降低時(shí)間效率。而且,目前越來(lái)越多腦機(jī)接口系統(tǒng)運(yùn)行于普適環(huán)境,而普適環(huán)境下很多設(shè)備的計(jì)算性能并不高,更容易遇到實(shí)時(shí)響應(yīng)瓶頸。因此,提高分類器的實(shí)時(shí)性能對(duì)于實(shí)時(shí)的應(yīng)用系統(tǒng)至關(guān)重要。在本文中,我們采用MPI (Message Passing Interface)并行計(jì)算的框架,將分類算法實(shí)現(xiàn)并行化,以降低分類過程的時(shí)間消耗,從而達(dá)到實(shí)時(shí)應(yīng)用的需要。
[Abstract]:Attention is a concept of psychology and a subject of research in brain science and bioinformatics. Monitoring people's attention can become an auxiliary tool for people to learn or work, thereby improving their learning and working efficiency, and even reducing the number of serious mistakes, such as learning in the process, If there is a tool to monitor students' attention, the teacher can remind the students when they are not focused, and the students can correct their mental state in time. In combination with computer science and biological information science, we can monitor human attention through brain-computer interfaces. It is a hot research topic to use computer to develop various kinds of BCI. However, at present, most of BCI's types are action types, that is to say, BCI is used to monitor what kind of action the brain is imagining. Then the Brain-Computer Interface (BCI) sends out the same action instructions to the external operation. The typical application of this BCI is the robot arm. However, there is a lack of research on the Brain-Computer Interface (BCI) for the mental state of monitoring attention in real time, because attention involves a lot of psychological knowledge and the research process is more complicated. The development of a real-time monitoring attention application system is less, more is offline processing of the brain computer interface, because the analysis and processing of brain waves is very complex, each processing step in the middle takes a certain amount of time. It is difficult to monitor in real time. In this paper, the current BCI technology and basic theory are deeply analyzed, and a real-time monitoring BCI is developed independently by absorbing the experience and theoretical knowledge of BCI development. And it is applied to monitor the attention of people in learning. The FIR algorithm based on Hanning window is used to extract six eigenvalues and the KNN (k-Nearest Neighbor) algorithm is used to classify the EEG signals. BCI needs a lot of training before it can be applied. Usually, a BCI needs several months' training from the beginning to the end. Therefore, the classification process will reduce the time efficiency with the increase of training data. Moreover, more and more BCI systems are running in pervasive environments, and many devices in pervasive environments have low computing performance, which makes it easier to meet the bottleneck of real-time response. Therefore, it is very important to improve the real-time performance of classifier for real-time application system. In this paper we use the framework of MPI (Message Passing Interface) parallel computing to parallelize the classification algorithm so as to reduce the time consumption of the classification process and achieve the need of real-time application.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP334.7

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