本文選題：腦電信號(hào) + 腦紋識(shí)別�。� 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：在個(gè)人信息安全愈加重要的當(dāng)今社會(huì),如何安全有效地進(jìn)行身份識(shí)別已經(jīng)成為一個(gè)重要話題�；谀X電信號(hào)的身份識(shí)別(腦紋識(shí)別)因此受到了越來(lái)越多的關(guān)注。區(qū)別于傳統(tǒng)身份識(shí)別特征所存在的各種各樣的缺陷,腦紋具有高隱蔽性、不可竊取性、不可仿制性以及必須活體等方面的獨(dú)特優(yōu)勢(shì)。目前基于腦電信號(hào)的身份識(shí)別研究大多要求執(zhí)行某種特定的任務(wù)或者需要在固定環(huán)境中采集腦電信號(hào)進(jìn)行分析。這些限制條件對(duì)腦紋識(shí)別的現(xiàn)實(shí)應(yīng)用發(fā)展有很大的局限性。本文著重研究了多任務(wù)狀態(tài)下的腦紋識(shí)別,對(duì)處于不同環(huán)境和執(zhí)行不同任務(wù)所采集到的腦電信號(hào),通過(guò)研究腦電信號(hào)的相位同步特征,利用腦功能網(wǎng)絡(luò)和深度信念網(wǎng)絡(luò)來(lái)研究多任務(wù)狀態(tài)下的腦紋識(shí)別。本文基于多個(gè)不同任務(wù)狀態(tài)的腦電數(shù)據(jù)集進(jìn)行腦紋識(shí)別研究,主要做了以下三方面的工作。1)本文提出將相位同步作為腦紋識(shí)別的公共特征提取方法,區(qū)別于傳統(tǒng)腦電信號(hào)特征提取針對(duì)一類任務(wù)使用一種特征提取方法,對(duì)于多類任務(wù)進(jìn)行腦紋識(shí)別時(shí)均使用相位同步進(jìn)行腦紋特征提取。同時(shí),對(duì)腦電信號(hào)相位同步進(jìn)行了研究分析并提出均值相鎖值對(duì)相位同步進(jìn)行測(cè)量。2)本文提出利用功能腦網(wǎng)絡(luò)進(jìn)行了二次腦紋特征提取。為了便于進(jìn)行腦紋識(shí)別,利用均值相鎖值構(gòu)建腦功能網(wǎng)絡(luò),將網(wǎng)絡(luò)屬性:節(jié)點(diǎn)的度,全局有效性,聚類系數(shù)進(jìn)行組合作為二次腦紋特征用于腦紋識(shí)別,并繪制腦地形圖觀察比較各項(xiàng)屬性在腦紋識(shí)別時(shí)所表現(xiàn)出的類內(nèi)的一致性和類間的差異性。同時(shí)研究發(fā)現(xiàn),腦網(wǎng)絡(luò)的全局有效性和聚類系數(shù)始終呈近似正相關(guān)趨勢(shì)。3)本文運(yùn)用深度信念網(wǎng)絡(luò)進(jìn)行多任務(wù)狀態(tài)下的腦紋識(shí)別。對(duì)不同任務(wù)和不同環(huán)境下的腦電信號(hào)利用公共特征提取方法相位同步進(jìn)行特征提取,訓(xùn)練網(wǎng)絡(luò)模型,并對(duì)網(wǎng)絡(luò)的各項(xiàng)參數(shù)進(jìn)行學(xué)習(xí)。研究發(fā)現(xiàn),對(duì)不同數(shù)據(jù)集進(jìn)行訓(xùn)練時(shí),一般要求不同的網(wǎng)絡(luò)層數(shù)和神經(jīng)元個(gè)數(shù),利用學(xué)習(xí)后的特征進(jìn)行分類,能夠進(jìn)行有效識(shí)別。同時(shí),在該部分,我們對(duì)一個(gè)含有兩種不同任務(wù)的混合型數(shù)據(jù)集進(jìn)行了腦紋識(shí)別研究,該混合數(shù)據(jù)集中的被試執(zhí)行兩種不同類型的任務(wù)。結(jié)果顯示,該方法針對(duì)不同任務(wù)的混合數(shù)據(jù)集也能進(jìn)行有效識(shí)別,對(duì)32名被試的腦紋識(shí)別準(zhǔn)確率超過(guò)96%。與以往基于腦電信號(hào)幅值研究的不同之處在于本文從信號(hào)的相位角度對(duì)基于腦紋的身份識(shí)別進(jìn)行了研究分析。本文創(chuàng)新性地將相位同步作為多任務(wù)狀態(tài)下腦紋識(shí)別的公共特征提取方法,并在五個(gè)數(shù)據(jù)集上進(jìn)行了腦紋識(shí)別實(shí)驗(yàn),對(duì)于9類,12類,14類,20類和32類數(shù)據(jù)集進(jìn)行識(shí)別均取得了比較好的腦紋識(shí)別效果,準(zhǔn)確率分別為99%,98%,99%,98%和96%。以上結(jié)果說(shuō)明了相位同步作為多任務(wù)腦電信號(hào)的公共特征提取方法是有效的。本文工作在一定程度上突破了腦紋識(shí)別中任務(wù)與環(huán)境的局限,使得基于腦紋的識(shí)別方法更具有泛化性,這對(duì)于如何改進(jìn)現(xiàn)有腦電身份識(shí)別方法大多限定于特定的任務(wù)或環(huán)境的局限性有一定的參考價(jià)值。
[Abstract]:In today's society, the security of personal information is becoming more and more important, how to identify the identity security and effectively has become an important topic. The identification of EEG based identification (brain pattern recognition) has attracted more and more attention. At present, most of the research on identification based on EEG requires the implementation of certain specific tasks or the need to collect EEG signals in a fixed environment. These limitations have great limitations on the development of the practical application of brain pattern recognition. This paper focuses on the study of brain pattern recognition in multi task state, the EEG signals collected in different environments and different tasks. By studying the phase synchronization characteristics of EEG signals, the brain pattern recognition under multi task state is studied by using brain function network and depth belief network. This paper is based on the number of EEG numbers in different task states. According to the research of brain pattern recognition, the main work has been done in the following three aspects. In this paper, the phase synchronization is used as the common feature extraction method of the brain pattern recognition, which is different from the traditional EEG feature extraction. A feature extraction method is used for a class of tasks, and the phase synchronization is used for all kinds of tasks in the brain pattern recognition. At the same time, the phase synchronization of the EEG signal is studied and analyzed and the mean phase lock value is proposed to measure phase synchronization.2. This paper proposes two brain pattern features extraction using functional brain network. In order to facilitate the brain pattern recognition, the mean phase lock value is used to construct the brain function network, and the network attribute: node degree, The global validity and clustering coefficient are used as the two pattern of brain pattern to identify the brain pattern, and draw a brain topographic map to observe the conformance and the difference between classes in the brain pattern recognition. At the same time, it is found that the global effectiveness and clustering coefficient of the brain network always have an approximate positive correlation trend.3). In this paper, the depth belief network is used to identify the brain pattern under multi task state. The EEG signals in different tasks and different environments are extracted by using the phase synchronization of the common feature extraction method. The network model is trained and the parameters of the network are studied. It is found that the training of different data sets is generally not required. The number of the same network layer and the number of neurons, using the characteristics of the learning to be classified, can be effectively identified. At the same time, in this part, we have carried out a brain pattern recognition study on a mixed data set containing two different tasks. The subjects of the mixed data set perform two different types of tasks. The results show that the method is directed against this method. The mixed data set of different tasks can also be effectively identified. The difference between the accuracy rate of the 32 subjects' brain pattern recognition is more than 96%. and the previous research on the amplitude of EEG signal based on the phase angle of the signal. This paper analyzes the identification of the brain based identification from the phase angle of the signal. This paper innovatively uses phase synchronization as a multi task state. The common feature extraction method of the lower brain pattern recognition, and the brain pattern recognition experiment on five data sets. The recognition results for the 9, 12, 14, 20 and 32 types of data sets are better. The accuracy rate is 99%, 98%, 99%, 98% and 96%., respectively, indicating the phase synchronization as the multi task EEG signal. The common feature extraction method is effective. This paper breaks through the limitations of the task and environment in the brain pattern recognition to a certain extent, making the recognition method based on the brain pattern more generalization, which is of certain reference value for how to improve the existing EEG identification methods to limit the limitations of specific tasks or environment.

【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7;TP391.41

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