發(fā)布時(shí)間：2019-03-11 08:56

【摘要】：近年來(lái),生物特征識(shí)別技術(shù)受到全世界各國(guó)的普遍關(guān)注,它在維護(hù)國(guó)家安全、個(gè)人信息安全、航空安全以及軍事、醫(yī)療等方面均發(fā)揮著重要的作用,成為信息化時(shí)代的前沿?zé)狳c(diǎn)課題。為了滿足特殊應(yīng)用場(chǎng)景的需求和彌補(bǔ)現(xiàn)有技術(shù)的不足,研究者們始終致力于開(kāi)發(fā)新的生物特征,腦電是其中較為新穎的嘗試之一�；谀X電的生物特征識(shí)別技術(shù)研究在國(guó)內(nèi)外尚屬起步階段,在腦電誘發(fā)范式的設(shè)計(jì)、腦電特征提取以及模式識(shí)別算法等方面仍存在較大的探索空間。 本研究從腦電的個(gè)體差異性出發(fā),首先針對(duì)現(xiàn)有研究中腦電誘發(fā)范式相對(duì)單一的問(wèn)題,設(shè)計(jì)并完成了涵蓋多種腦電誘發(fā)范式的兩類事件任務(wù)實(shí)驗(yàn):第一類任務(wù)包括靜息、視覺(jué)認(rèn)知、計(jì)算任務(wù)和運(yùn)動(dòng)想象四種范式,樣本量20人;第二類任務(wù)是視覺(jué)誘發(fā)P3范式,樣本量8人。為了有效地提取腦電中的個(gè)體差異性信息,針對(duì)各范式誘發(fā)腦電的信號(hào)特點(diǎn),研究中分別嘗試了AR模型、時(shí)域能量譜、頻域能量譜、相位鎖定值以及相干平均等多種時(shí)域和頻域的特征提取算法,并利用支持向量機(jī)對(duì)上述各種范式的腦電特征進(jìn)行分類識(shí)別,從而得到基于全樣本的統(tǒng)計(jì)分類正確率。本文研究結(jié)果表明,誘發(fā)腦電的個(gè)體差異性明顯高于靜息腦電,而且越是復(fù)雜任務(wù)的、被試參與度高的、與思維活動(dòng)密切相關(guān)的范式,其誘發(fā)的腦電個(gè)體差異性越明顯,分類正確率最高可達(dá)98%以上,從而驗(yàn)證了腦電可用于生物特征識(shí)別的可行性。 在此基礎(chǔ)上,為了進(jìn)一步提高系統(tǒng)性能和識(shí)別效率,本研究在特征篩選和導(dǎo)聯(lián)優(yōu)化方面做了初步探索,并通過(guò)遺傳算法、Fisher判別率和遞歸特征篩選三種方法對(duì)分類器進(jìn)行了優(yōu)化,與優(yōu)化前相比不僅識(shí)別率有所提高,而且導(dǎo)聯(lián)數(shù)得到明顯簡(jiǎn)化,從而為今后腦電的個(gè)體差異性分析及其面向生物特征識(shí)別的實(shí)用性設(shè)計(jì)提供了新的思路。
[Abstract]:In recent years, biometric identification technology has been widely concerned all over the world. It plays an important role in maintaining national security, personal information security, aviation security, military affairs, medical treatment and so on. It has become a hot topic in the information age. In order to meet the needs of special application scenarios and make up for the shortcomings of existing technologies, researchers have always been devoted to the development of new biological characteristics, among which EEG is one of the more novel attempts. The research of EEG-based biometric recognition technology is still in its infancy at home and abroad. There is still a large exploration space in the design of EEG-induced paradigm, EEG feature extraction and pattern recognition algorithm. Based on the individual differences of EEG, this study designs and completes two kinds of event task experiments covering multiple EEG evoked paradigms: the first type of tasks includes resting tasks, aiming at the problem that EEG evoked paradigm is relatively single in the existing studies, and two kinds of event task experiments covering various EEG evoked paradigms are designed and completed. Visual cognition, computational tasks and motor imagination four paradigms, sample size of 20; The second type of task was the visual evoked P3 paradigm, with a sample size of 8. In order to extract the individual difference information effectively, the AR model, the time domain energy spectrum and the frequency domain energy spectrum are tried in the study, according to the signal characteristics of the evoked EEG in each normal form. A variety of time-domain and frequency-domain feature extraction algorithms such as phase-locked values and coherent averages are used to classify and recognize EEG features from the above-mentioned paradigms using support vector machines to obtain the statistical classification accuracy rate based on full samples. The results of this study show that the individual difference of evoked EEG is significantly higher than that of resting EEG, and the more complex tasks, the higher the participation of subjects and the paradigm closely related to thinking activity, and the more obvious the individual difference of evoked EEG is, the more complex the task is, the higher the participants' participation is, and the more closely related to thinking activities. The correct rate of classification is up to 98%, which proves the feasibility of EEG in biometric recognition. On this basis, in order to further improve the system performance and recognition efficiency, this study has made a preliminary exploration in feature screening and lead optimization, and through genetic algorithm, Three methods of Fisher discrimination rate and recursive feature selection are used to optimize the classifier. Compared with the pre-optimization, the recognition rate is improved and the lead number is obviously simplified. It provides a new idea for the analysis of individual difference of EEG and the practical design for biometric recognition.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.0

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本文編號(hào)：2438149