本文選題：腦電信號 + 腦紋識別��； 參考：《杭州電子科技大學》2017年碩士論文

【摘要】：在個人信息安全愈加重要的當今社會,如何安全有效地進行身份識別已經成為一個重要話題�；谀X電信號的身份識別(腦紋識別)因此受到了越來越多的關注。區(qū)別于傳統(tǒng)身份識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特優(yōu)勢。目前基于腦電信號的身份識別研究大多要求執(zhí)行某種特定的任務或者需要在固定環(huán)境中采集腦電信號進行分析。這些限制條件對腦紋識別的現(xiàn)實應用發(fā)展有很大的局限性。本文著重研究了多任務狀態(tài)下的腦紋識別,對處于不同環(huán)境和執(zhí)行不同任務所采集到的腦電信號,通過研究腦電信號的相位同步特征,利用腦功能網(wǎng)絡和深度信念網(wǎng)絡來研究多任務狀態(tài)下的腦紋識別。本文基于多個不同任務狀態(tài)的腦電數(shù)據(jù)集進行腦紋識別研究,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別的公共特征提取方法,區(qū)別于傳統(tǒng)腦電信號特征提取針對一類任務使用一種特征提取方法,對于多類任務進行腦紋識別時均使用相位同步進行腦紋特征提取。同時,對腦電信號相位同步進行了研究分析并提出均值相鎖值對相位同步進行測量。2)本文提出利用功能腦網(wǎng)絡進行了二次腦紋特征提取。為了便于進行腦紋識別,利用均值相鎖值構建腦功能網(wǎng)絡,將網(wǎng)絡屬性:節(jié)點的度,全局有效性,聚類系數(shù)進行組合作為二次腦紋特征用于腦紋識別,并繪制腦地形圖觀察比較各項屬性在腦紋識別時所表現(xiàn)出的類內的一致性和類間的差異性。同時研究發(fā)現(xiàn),腦網(wǎng)絡的全局有效性和聚類系數(shù)始終呈近似正相關趨勢。3)本文運用深度信念網(wǎng)絡進行多任務狀態(tài)下的腦紋識別。對不同任務和不同環(huán)境下的腦電信號利用公共特征提取方法相位同步進行特征提取,訓練網(wǎng)絡模型,并對網(wǎng)絡的各項參數(shù)進行學習。研究發(fā)現(xiàn),對不同數(shù)據(jù)集進行訓練時,一般要求不同的網(wǎng)絡層數(shù)和神經元個數(shù),利用學習后的特征進行分類,能夠進行有效識別。同時,在該部分,我們對一個含有兩種不同任務的混合型數(shù)據(jù)集進行了腦紋識別研究,該混合數(shù)據(jù)集中的被試執(zhí)行兩種不同類型的任務。結果顯示,該方法針對不同任務的混合數(shù)據(jù)集也能進行有效識別,對32名被試的腦紋識別準確率超過96%。與以往基于腦電信號幅值研究的不同之處在于本文從信號的相位角度對基于腦紋的身份識別進行了研究分析。本文創(chuàng)新性地將相位同步作為多任務狀態(tài)下腦紋識別的公共特征提取方法,并在五個數(shù)據(jù)集上進行了腦紋識別實驗,對于9類,12類,14類,20類和32類數(shù)據(jù)集進行識別均取得了比較好的腦紋識別效果,準確率分別為99%,98%,99%,98%和96%。以上結果說明了相位同步作為多任務腦電信號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別中任務與環(huán)境的局限,使得基于腦紋的識別方法更具有泛化性,這對于如何改進現(xiàn)有腦電身份識別方法大多限定于特定的任務或環(huán)境的局限性有一定的參考價值。
[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.

【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7;TP391.41

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