本文選題：運動觀察　切入點：腦電信號　出處：《鄭州大學》2017年碩士論文

【摘要】：運動觀察作為人腦的一種認知活動,對運動觀察過程中腦電(Electroencephalogram,EEG)信號的研究,有利于對人腦工作機制的探索。而且通過對不同運動觀察過程EEG的特征提取與識別,在軍事偵查、目標追蹤中也有很大的應(yīng)用價值,也為腦-機接口系統(tǒng)設(shè)計提供一種新的思路。然而在運動觀察過程中,由于大腦沒有主動的思維任務(wù)參與,無法通過EEG直接確定是否處于有效運動觀察狀態(tài),且與運動想象、穩(wěn)態(tài)視覺誘發(fā)電位相比,運動觀察過程的腦電信號幅值更弱,更加難以獲取。本文以實現(xiàn)觀察小車左轉(zhuǎn)、右轉(zhuǎn)過程的腦電信號特征解析與識別為目的,首先采用SMI眼動儀和Neuroscan腦電設(shè)備同步采集信號,設(shè)計了觀察小車左轉(zhuǎn)、右轉(zhuǎn)兩種狀態(tài)的實驗范式,利用眼動軌跡信號分析來確定有效運動觀察任務(wù)。從時頻角度分析有效運動觀察過程的激活腦區(qū)和不同頻段能量譜分布,確定特征明顯頻段。由于人腦在認知活動中,其神經(jīng)元之間存在有向信息的交互,進一步采用能夠描述不同腦區(qū)間信息流向的因果網(wǎng)絡(luò)分析方法,通過分析運動觀察過程中因果網(wǎng)絡(luò)的網(wǎng)絡(luò)測度,找到其差異性,并對差異性明顯的網(wǎng)絡(luò)測度進行分類。最后,利用CSP和SVM算法對運動觀察EEG特征進行識別。主要研究內(nèi)容如下:(1)針對運動觀察EEG高度非平穩(wěn)低信噪比的特點,以研究獲取運動觀察過程中腦電特征明顯頻段作為切入點,對有效任務(wù)的EEG進行預(yù)處理,提高EEG信噪比;然后,對不同頻段EEG進行腦地形圖分析,定位激活腦區(qū)、確定關(guān)鍵通道;最后,運用WPT和功率譜融合的方法,分析關(guān)鍵通道EEG在不同頻段范圍內(nèi)的能量譜分布,確定特征明顯頻段。結(jié)果顯示:特征明顯頻段為0.49-0.98Hz。(2)基于不同腦區(qū)間存在信息流,采用因果網(wǎng)絡(luò)測度差異性分析的方法研究運動觀察信號特征,利用GC、DTF、PDC三種分析方法對不同頻段EEG進行因果網(wǎng)絡(luò)構(gòu)建。通過分析不同閾值下因果網(wǎng)絡(luò)的網(wǎng)絡(luò)密度和全局效率,選擇合適的閾值,并分析網(wǎng)絡(luò)測度(包括度、聚類系數(shù)、全局效率)的差異性。結(jié)果表明,在0-4Hz上,GC值的聚類系數(shù)具有顯著性差異。(3)針對運動觀察過程中腦電特征的識別問題,利用CSP算法對EEG進行濾波,以濾波后的信號能量為特征,并采用SVM進行特征識別,比較了不同頻段上EEG的分類識別率,最高為0-4Hz上的86.15%。最后,通過對通道進行優(yōu)化,可以在較少通道的情況下實現(xiàn)較高的分類準確率,并實現(xiàn)了基于GC的聚類系數(shù)特征的分類識別。
[Abstract]:As a cognitive activity of human brain, the study of electroencephalograms (EGG) signals in the course of motion observation is beneficial to the exploration of the working mechanism of human brain. Moreover, by extracting and recognizing the characteristics of EEG in different motion observation processes, it can be used in military investigation. Target tracking also has great application value and provides a new way of thinking for the design of brain-computer interface system. However, in the process of motion observation, the brain has no active thinking task to participate. It is impossible to determine directly by EEG whether it is in an effective state of motion observation, and the amplitude of EEG during exercise observation is weaker and more difficult to obtain than that of motion imagination. For the purpose of analyzing and recognizing the characteristics of EEG signals in the process of right turn, the SMI eye movement instrument and the Neuroscan EEG equipment are used to synchronize the acquisition of signals, and an experimental paradigm is designed to observe the two states of the vehicle turning left and right. The effective motion observation task is determined by using eye movement track signal analysis. The active brain region and the energy spectrum distribution of different frequency bands in the effective motion observation process are analyzed from the angle of time and frequency, and the characteristic obvious frequency band is determined. Because the human brain is in the cognitive activity, There is the interaction of directed information between neurons. The causal network analysis method, which can describe the flow of information in different brain regions, is further used to find the difference of the causal network through analyzing the network measurement of the causal network in the course of motion observation. Finally, CSP and SVM algorithms are used to recognize the feature of motion observation EEG. The main research contents are as follows: 1) aiming at the characteristics of high non-stationary and low signal-to-noise ratio (SNR) of motion observation EEG, In order to improve the signal-to-noise ratio (SNR) of EEG, the EEG of effective task is pretreated with the obvious frequency band of EEG characteristics in the course of motion observation. Then, the brain topographic map of EEG in different frequency bands is analyzed to locate and activate the brain region. Finally, using the method of WPT and power spectrum fusion, the energy spectrum distribution of critical channel EEG in different frequency range is analyzed. The results show that the obvious characteristic frequency range is 0.49-0.98Hz.z.t2) based on the existence of information flow in different brain regions, the motion observation signal features are studied by using the method of difference analysis of causal network measure. The causality network of EEG in different frequency bands is constructed by using three analysis methods. By analyzing the network density and global efficiency of causality network under different thresholds, the appropriate threshold is selected, and the network measure (including degree, clustering coefficient) is analyzed. The results show that the clustering coefficients of GC values on 0-4Hz have significant difference. Aiming at the problem of EEG feature recognition during motion observation, the EEG is filtered by CSP algorithm, which is characterized by the filtered signal energy. The recognition rate of EEG in different frequency bands is compared with that of 86.15 on 0-4Hz. Finally, by optimizing the channel, the classification accuracy can be achieved in the case of fewer channels. The classification and recognition of clustering coefficient features based on GC are realized.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R318;TN911.7

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