本文選題：腦電信號 + 眼電偽跡　； 參考：《大連理工大學(xué)》2016年博士論文

【摘要】：腦電信號能夠反映人的心理狀態(tài)和大腦的生理功能,被廣泛地應(yīng)用于心理分析以及臨床疾病診斷等研究領(lǐng)域。腦電信號在采集過程中容易受到其它生理信號的干擾,其中具有不可控性的眼電信號的干擾(即眼電偽跡)最為嚴(yán)重。因此,有效去除腦電信號中的眼電偽跡對信號分析具有重要意義。本文在盲源信號分離方法的基礎(chǔ)上,針對眼電偽跡去除問題展開研究。研究內(nèi)容包括以下三個方面：(1)提出基于參數(shù)模型的眼電偽跡識別方法。首先,研究正常人腦電信號中眼電偽跡識別問題,針對眼電偽跡具有非高斯性,建立異方差轉(zhuǎn)移混合分布模型,利用條件期望最大化算法估計該模型的參數(shù),獲得眼電偽跡特征,從而區(qū)分正常人腦電信號和眼電偽跡。其次,針對癲癇病人腦電信號和眼電偽跡識別問題,由于兩者都具有非高斯特點,上述模型難以有效解決該問題,則根據(jù)癲癇腦電信號相位同步特點,利用極限學(xué)習(xí)機建立信號的瞬時相位模型,將模型的輸出權(quán)值作為信號特征以用于區(qū)分癲癇病人腦電信號和眼電偽跡,同時采用希爾伯特-黃變換解決非平穩(wěn)信號瞬時相位難以獲取的問題。(2)提出基于高階統(tǒng)計標(biāo)準(zhǔn)(Candecomp/Parafac, CP)張量欠定盲分離的眼電偽跡去除方法。高階統(tǒng)計CP張量模型分解唯一性的特點可以保證在眼電偽跡去除欠定盲分離過程中源信號的估計具有唯一解。針對欠定盲分離中觀察信號具有較強相關(guān)性導(dǎo)致源信號難以估計問題,引入主成分分析方法降低觀察信號的二階相關(guān)性,使信號集中于高階統(tǒng)計分析中,利用觀察信號的主成分陣構(gòu)建出高階統(tǒng)計CP張量模型并分解,從而提高欠定盲分離源信號估計性能。對于欠定盲分離中混合矩陣的非負(fù)性問題,采用一種基于正則化分層交替最小二乘方法分解高階統(tǒng)計CP張量模型,保證模型分解過程為非負(fù)分解過程,進而求出欠定盲分離中的非負(fù)混合矩陣。(3)提出基于高階統(tǒng)計Tucker張量欠定盲分離的眼電偽跡去除方法。針對眼電偽跡深入隱藏在腦電信號中難以有效分離的問題,利用Tucker張量模型中核張量能挖掘隱變量信息的特點,采用高階統(tǒng)計Tucker張量模型,實現(xiàn)眼電偽跡去除中的欠定盲分離過程。由于高階統(tǒng)計Tucker張量模型難以分解,采用分層交替最小二乘(Hierarchical Alternating Least Squares, HALS)算法提高模型分解速度。在此基礎(chǔ)上,改善高階統(tǒng)計Tucker張量欠定盲分離非平穩(wěn)源信號估計性能,在高階統(tǒng)計Tucker模型建立過程中引入傅里葉變換,構(gòu)造一種時頻高階統(tǒng)計Tucker模型并結(jié)合最小殘差共軛梯度算法,提高非平穩(wěn)源信號估計的準(zhǔn)確度。
[Abstract]:EEG signals can reflect the psychological state and physiological function of human brain and are widely used in the field of psychoanalysis and the diagnosis of clinical diseases. EEG signals are easily disturbed by other physiological signals in the process of acquisition, especially the uncontrollable Eye-electric signals (i.e., Eye-electric artifact). Therefore, the removal of Eye-electric artifacts from EEG signals is of great significance to signal analysis. On the basis of blind source signal separation method, the problem of eye electrical artifact removal is studied in this paper. The research includes the following three aspects: 1) an eye electrical artifact recognition method based on parametric model is proposed. First of all, the problem of Eye-electric artifact recognition in normal human brain electrical signals is studied. Aiming at the non-Gao Si property of Eye-electric artifact, a mixed heteroscedasticity transfer distribution model is established, and the parameters of the model are estimated by using conditional expectation maximization algorithm. Eye-electric artifact features are obtained to distinguish normal human brain electrical signals from Eye-electric artifacts. Secondly, in view of the problem of EEG and Eye-electric artifact recognition in epileptic patients, the above model is difficult to solve the problem effectively because both of them have the characteristics of non-Gao Si, and then according to the characteristics of phase synchronization of epileptic EEG signals, The instantaneous phase model of the signal was established by using the extreme learning machine, and the output weight of the model was taken as the signal feature to distinguish the EEG signal from the eye electrical artifact in the epileptic patient. At the same time, Hilbert-Huang transform is used to solve the problem that the instantaneous phase of non-stationary signal is difficult to get. The uniqueness of higher-order statistical CP Zhang Liang model can guarantee the unique solution of source signal estimation in the process of ocular-electric artifact removal from under-determined blind separation. In order to reduce the second-order correlation of observation signals and concentrate them on high-order statistical analysis, it is difficult to estimate the source signals due to the strong correlation of observation signals in under-determined blind separation, so that the principal component analysis (PCA) method is introduced to reduce the second-order correlation of observation signals. The higher-order statistical CP Zhang Liang model is constructed by using the principal component matrix of the observed signal and decomposed to improve the performance of the under-determined blind source estimation. For the problem of nonnegativity of mixed matrix in underdetermined blind separation, a regularized hierarchical alternating least-squares method is used to decompose the higher-order statistical CP Zhang Liang model to ensure that the decomposition process of the model is a non-negative decomposition process. Furthermore, the nonnegative mixed matrix in underdetermined blind separation is obtained. (3) an eye electrical artifact removal method based on high-order statistical Tucker Zhang Liang subblind separation is proposed. Aiming at the problem that Eye-electric artifact is difficult to be separated effectively in EEG signal, using kernel tensor in Tucker Zhang Liang model to mine hidden variable information, high order statistical Tucker Zhang Liang model is adopted. The process of undetermined blind separation in the removal of eye electrical artifacts is realized. Because the higher-order statistical Tucker Zhang Liang model is difficult to decompose, the hierarchical alternating least square algorithm is used to improve the decomposition speed of the model. On this basis, the estimation performance of high order statistical Tucker Zhang Liang underdetermined blind source signal is improved, and Fourier transform is introduced in the process of establishing high order statistical Tucker model. A time-frequency high-order statistical Tucker model is constructed and the minimum residual conjugate gradient algorithm is used to improve the accuracy of non-stationary source signal estimation.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN911.7;R338
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本文編號：1991608