【摘要】：腦電信號(hào)中包含豐富的信息，經(jīng)常應(yīng)用在工程的腦機(jī)接口以及臨床的疾病診斷中。能否很好地對(duì)腦電信號(hào)進(jìn)行分析、準(zhǔn)確快速地提取信息決定腦電信號(hào)在應(yīng)用過(guò)程中性能的好壞。本文在對(duì)國(guó)內(nèi)外的腦電信號(hào)分析方法進(jìn)行研究后，利用近年來(lái)信號(hào)處理領(lǐng)域中應(yīng)用較廣泛的盲源分離算法，分別以P300腦電信號(hào)和運(yùn)動(dòng)想象腦電信號(hào)為對(duì)象，對(duì)腦電信號(hào)進(jìn)行分析。 P300腦電信號(hào)強(qiáng)度很弱，容易受到環(huán)境以及眼動(dòng)偽跡、心電、肌電和自發(fā)腦電信號(hào)的干擾，淹沒(méi)在采集腦電信號(hào)中。為了快速高效地將P300腦電信號(hào)與各種干擾分離開(kāi)來(lái)，本文通過(guò)分析P300時(shí)域、頻域和頭皮空間域的特點(diǎn)，提出以相干平均、小波變換與盲源分離相結(jié)合的算法，從時(shí)頻域和頭皮空間域?qū)300腦電信號(hào)進(jìn)行提取。并且提出一種可以自動(dòng)地從盲源分離獲得的多個(gè)源信號(hào)估計(jì)分量中選取P300對(duì)應(yīng)分量的方法。設(shè)計(jì)實(shí)驗(yàn)對(duì)比分析三種盲源分離算法——Informax、FastICA和AMUSE在P300腦電信號(hào)提取過(guò)程中的性能。通過(guò)實(shí)驗(yàn)表明，基于盲源分離的P300腦電信號(hào)提取方法相比于僅從時(shí)頻域進(jìn)行提取的性能有顯著提高。 針對(duì)如何準(zhǔn)確有效地提取運(yùn)動(dòng)想象腦電信號(hào)特征的問(wèn)題，本文通過(guò)分析運(yùn)動(dòng)想象腦電信號(hào)時(shí)域、頻域和頭皮空間域的特征，提出以小波變換為預(yù)處理，并利用二階盲辨識(shí)(SOBI)算法和信息論特征提取(ITFE)算法相結(jié)合獲得的空間濾波器，從時(shí)域、頻域和頭皮空間域提取運(yùn)動(dòng)想象腦電信號(hào)的方法，并將能量作為特征。通過(guò)實(shí)驗(yàn)表明基于盲源分離的運(yùn)動(dòng)想象腦電信號(hào)特征提取方法具有一定優(yōu)越性，，盲源分離算法SOBI與ITFE相結(jié)合獲得的空間濾波器能夠反映更真實(shí)的大腦源活動(dòng)。
[Abstract]:EEG signals contain a wealth of information and are often used in engineering brain-computer interfaces and clinical disease diagnosis. Whether the EEG signal can be well analyzed and the information extracted accurately and quickly determines the performance of EEG signal in the process of application. After studying the methods of EEG analysis at home and abroad, this paper uses the blind source separation algorithm, which is widely used in the field of signal processing in recent years, to take the P300 EEG signal and the motion imaginary EEG signal as the objects, respectively. The intensity of P300 EEG signal is very weak, which is easily disturbed by environment, eye movement artifacts, ECG, EMG and spontaneous EEG signals, and is submerged in the collection of EEG signals. In order to separate P300 EEG signal from all kinds of interference quickly and efficiently, by analyzing the characteristics of P300 time domain, frequency domain and scalp space domain, this paper proposes an algorithm combining coherent averaging, wavelet transform and blind source separation. The P300 EEG signals were extracted from time-frequency domain and scalp spatial domain. A method for automatically selecting P300 corresponding components from the estimated components of multiple sources obtained by blind source separation is proposed. The performance of three blind source separation algorithms, Informax-FastICA and AMUSE, in the process of P300 EEG signal extraction is compared and analyzed. Experiments show that the performance of P300 EEG signal extraction method based on blind source separation is significantly improved compared with that of extracting P300 EEG signal from time and frequency domain only. In order to solve the problem of how to extract the features of motion imagination EEG accurately and effectively, this paper proposes wavelet transform as the preprocessing method by analyzing the features of motion imaginary EEG in time domain, frequency domain and scalp space domain. The second order blind identification (SOBI) algorithm and the information theory feature extraction (ITFE) algorithm are used to extract the motion imaginary EEG signals from the time domain, frequency domain and scalp spatial domain, with energy as the feature. Experiments show that the feature extraction method based on blind source separation has some advantages, and the spatial filter based on SOBI and ITFE can reflect more real brain source activity.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.6

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