盲信號(hào)分離在腦電信號(hào)偽跡去除中的應(yīng)用
發(fā)布時(shí)間：2018-08-21 09:48
【摘要】：腦-機(jī)接口技術(shù)的核心思想在于將輸入的觀測(cè)腦電信號(hào)轉(zhuǎn)換為輸出的控制信號(hào),從而驅(qū)動(dòng)計(jì)算機(jī)設(shè)備。通過受試者頭皮電極采集到的腦電(EEG,Electroencephalogram)非常微弱,并且伴隨多種偽跡(Artifact)的干擾,給腦電信號(hào)的特征提取和后續(xù)分析增加了更大的難度。盲信號(hào)分離(BSS,Blind Source Separation)是在通信系統(tǒng)的輸入和傳輸信道均未知的情況下提出來的,即對(duì)源信號(hào)的先驗(yàn)知識(shí)少知或不知,對(duì)傳輸信道特性也未知。本課題針對(duì)腦電信號(hào)處理中的問題,對(duì)基于BSS思想的自動(dòng)去除EEG中偽跡的方法展開了研究。本文首先對(duì)腦電信號(hào)偽跡分離的研究背景和國(guó)內(nèi)外研究狀況做了介紹,然后學(xué)習(xí)了腦電信號(hào)的基本知識(shí),詳細(xì)闡述了腦電信號(hào)與偽跡信號(hào)的特性與分類,研究中著重考慮對(duì)腦電信號(hào)影響最嚴(yán)重的眼電偽跡和50Hz的工頻干擾。其次介紹了盲信號(hào)分離的核心思想,其用于解決腦電信號(hào)偽跡分離問題時(shí)的數(shù)學(xué)模型、約束條件和預(yù)處理過程,還深入學(xué)習(xí)了盲信號(hào)分離的經(jīng)典算法(JADE,FastICA)。在分析了傳統(tǒng)算法局限性的基礎(chǔ)之上,進(jìn)一步尋求了腦電信號(hào)領(lǐng)域的一種全新的解決問題思路,本文首次嘗試將Stone's BSS算法引入EEG信號(hào)處理領(lǐng)域,為腦電信號(hào)的偽跡去除引入了新方法。Stone's BSS突破了以往信號(hào)處理方法中要求源信號(hào)不能服從高斯分布和相互獨(dú)立的局限性,只要求混合信號(hào)是時(shí)間可預(yù)測(cè)的,分別采用長(zhǎng)、短濾波預(yù)測(cè)對(duì)混合信號(hào)作用,將BSS問題轉(zhuǎn)變成一個(gè)廣義特征分解問題,從而求得解混矩陣。文中還對(duì)Stone's BSS進(jìn)行了改進(jìn),引入遺傳算法用于對(duì)長(zhǎng)、短濾波調(diào)諧,使之成為一種成熟穩(wěn)定的算法。緊接著選取了一組具有代表性的模擬信號(hào)對(duì)改進(jìn)的Stone's BSS與其他BSS方法的分離結(jié)果做了對(duì)比,理論上證明了改進(jìn)算法在高斯型、亞高斯型信號(hào)分離中的良好性能。最后,結(jié)合目標(biāo)信號(hào)的特征和性質(zhì),通過不同的實(shí)際數(shù)據(jù)對(duì)改進(jìn)的Stone's BSS算法在EEG中的眨眼偽跡(EOG,Electrooculogram)和工頻干擾的分離展開了大量實(shí)驗(yàn)研究,選用恰當(dāng)?shù)脑u(píng)價(jià)指標(biāo)對(duì)結(jié)果進(jìn)行分析判斷,結(jié)果表明改進(jìn)算法和傳統(tǒng)算法都能夠完成EEG中的偽跡分離,改進(jìn)的Stone's BSS算法表現(xiàn)出更好地性能。本研究的工作也為Stone算法在生物信號(hào)領(lǐng)域的應(yīng)用奠定了重要基礎(chǔ)。結(jié)尾概括全文給出結(jié)論,并指出課題下一步可繼續(xù)拓展深入研究的方向。
[Abstract]:The core idea of brain-computer interface technology is to convert the input observation EEG signal into the output control signal, so as to drive the computer equipment. The EEG electroencephalogram (EEG) collected by the scalp electrode is very weak, and accompanied by the interference of many artifacts (Artifact), it is more difficult to extract and analyze the EEG features. Blind signal separation (BSS) Blind Source Separation) is proposed when the input and transmission channels of the communication system are unknown, that is, the prior knowledge of the source signal is little or unknown, and the characteristics of the transmission channel are also unknown. In order to solve the problem of EEG signal processing, the method of automatically removing artifacts in EEG based on BSS is studied in this paper. In this paper, the background of EEG artifact separation and the research status at home and abroad are introduced, then the basic knowledge of EEG is studied, and the characteristics and classification of EEG and artifact are described in detail. Eye-electric artifacts and power frequency interference of 50Hz, which have the most serious effect on EEG, are considered in this study. Secondly, the core idea of blind signal separation is introduced, which is used to solve the problem of EEG artifact separation. The mathematical model, constraint conditions and pretreatment process are also introduced. The classical algorithm of blind signal separation (JADEN FastICA) is also studied in depth. On the basis of analyzing the limitation of the traditional algorithm, a new way of solving the problem in the field of EEG signal is further sought. In this paper, the Stone's BSS algorithm is introduced into the field of EEG signal processing for the first time. In this paper, a new method for removing artifacts of EEG signals is introduced. Stonews BSS breaks through the limitation of the previous signal processing methods that the source signals cannot be distributed from Gao Si and independent of each other, and only requires that the mixed signals be predictable in time and use long time, respectively. The BSS problem is transformed into a generalized eigenvalue decomposition problem by short filter prediction to the mixed signal, and the unmixing matrix is obtained. In this paper, Stone's BSS is improved, and genetic algorithm is introduced to tune long and short filter, which makes it a mature and stable algorithm. Then a group of representative analog signals are selected to compare the results of the improved Stone's BSS and other BSS methods. It is proved theoretically that the improved algorithm has good performance in the separation of Gao Si type and subGao Si type signals. Finally, according to the characteristics and properties of the target signal, a large number of experiments on the separation of the improved Stone's BSS algorithm in EEG and the power frequency interference are carried out through different actual data. The results show that both the improved algorithm and the traditional algorithm can separate artifacts in EEG, and the improved Stone's BSS algorithm shows better performance. The work of this study also lays an important foundation for the application of Stone algorithm in the field of biological signals. At the end of this paper, the conclusion is given, and the further research direction is pointed out.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7;R318
本文編號(hào)：2195330
[Abstract]:The core idea of brain-computer interface technology is to convert the input observation EEG signal into the output control signal, so as to drive the computer equipment. The EEG electroencephalogram (EEG) collected by the scalp electrode is very weak, and accompanied by the interference of many artifacts (Artifact), it is more difficult to extract and analyze the EEG features. Blind signal separation (BSS) Blind Source Separation) is proposed when the input and transmission channels of the communication system are unknown, that is, the prior knowledge of the source signal is little or unknown, and the characteristics of the transmission channel are also unknown. In order to solve the problem of EEG signal processing, the method of automatically removing artifacts in EEG based on BSS is studied in this paper. In this paper, the background of EEG artifact separation and the research status at home and abroad are introduced, then the basic knowledge of EEG is studied, and the characteristics and classification of EEG and artifact are described in detail. Eye-electric artifacts and power frequency interference of 50Hz, which have the most serious effect on EEG, are considered in this study. Secondly, the core idea of blind signal separation is introduced, which is used to solve the problem of EEG artifact separation. The mathematical model, constraint conditions and pretreatment process are also introduced. The classical algorithm of blind signal separation (JADEN FastICA) is also studied in depth. On the basis of analyzing the limitation of the traditional algorithm, a new way of solving the problem in the field of EEG signal is further sought. In this paper, the Stone's BSS algorithm is introduced into the field of EEG signal processing for the first time. In this paper, a new method for removing artifacts of EEG signals is introduced. Stonews BSS breaks through the limitation of the previous signal processing methods that the source signals cannot be distributed from Gao Si and independent of each other, and only requires that the mixed signals be predictable in time and use long time, respectively. The BSS problem is transformed into a generalized eigenvalue decomposition problem by short filter prediction to the mixed signal, and the unmixing matrix is obtained. In this paper, Stone's BSS is improved, and genetic algorithm is introduced to tune long and short filter, which makes it a mature and stable algorithm. Then a group of representative analog signals are selected to compare the results of the improved Stone's BSS and other BSS methods. It is proved theoretically that the improved algorithm has good performance in the separation of Gao Si type and subGao Si type signals. Finally, according to the characteristics and properties of the target signal, a large number of experiments on the separation of the improved Stone's BSS algorithm in EEG and the power frequency interference are carried out through different actual data. The results show that both the improved algorithm and the traditional algorithm can separate artifacts in EEG, and the improved Stone's BSS algorithm shows better performance. The work of this study also lays an important foundation for the application of Stone algorithm in the field of biological signals. At the end of this paper, the conclusion is given, and the further research direction is pointed out.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7;R318
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