【摘要】：運(yùn)動(dòng)行為的神經(jīng)信號(hào)解碼研究是腦-機(jī)接口研究的重要內(nèi)容,解碼生物的運(yùn)動(dòng)意圖,具有重要的理論與實(shí)際應(yīng)用價(jià)值。微電極陣列記錄到的信號(hào)包括鋒電位信號(hào)(spike)和局部場電位信號(hào)(Local field potentials,LFP),關(guān)于spike信號(hào)的運(yùn)動(dòng)解碼研究取得了一定成果,但是隨著電極植入的時(shí)間增長,spike信號(hào)質(zhì)量有所下降,而局部場電位具有長期解碼穩(wěn)定的特點(diǎn),逐漸引起研究人員的關(guān)注。但是,大腦結(jié)構(gòu)復(fù)雜,某一運(yùn)動(dòng)行為的執(zhí)行可能由大腦多個(gè)腦區(qū)共同作用,運(yùn)動(dòng)意圖在腦中的存在時(shí)間非常短暫,采集到的局部場電位為非平穩(wěn)信號(hào),且在記錄的過程中不可避免的會(huì)引入噪聲信號(hào),這些都對解碼特征的有效提取造成一定阻礙。在對大腦信息進(jìn)行解碼時(shí),關(guān)鍵是確定出有效編碼信息的時(shí)間及頻率窗口,提取到利于解碼的特征。本文以鴿子為研究對象,結(jié)合動(dòng)物行為學(xué)方法與神經(jīng)信號(hào)微電極陣列檢測技術(shù),采集了鴿子左轉(zhuǎn)、直行、右轉(zhuǎn)三個(gè)方向轉(zhuǎn)向運(yùn)動(dòng)發(fā)生時(shí)NCL(Nidopallium Caudolaterale)腦區(qū)的LFP信號(hào)。對LFP信號(hào)特征提取方法進(jìn)行分析,探討鴿子運(yùn)動(dòng)轉(zhuǎn)向時(shí)的LFP信號(hào)的特征變化,并用k近鄰(k-Nearest Neighbor,kNN)方法對提取的特征進(jìn)行分類,預(yù)測其運(yùn)動(dòng)方向。主要工作如下:1.對局部場電位信號(hào)的產(chǎn)生過程,局部場電位信號(hào)特性及相關(guān)噪聲特性進(jìn)行了分析,在此基礎(chǔ)上比較了常用處理方法在LFP信號(hào)去噪中的優(yōu)缺點(diǎn),以及常用時(shí)域,頻域時(shí)頻域方法在特征提取方面的優(yōu)缺點(diǎn)。2.采用了一種結(jié)合獨(dú)立成分分析(Independent component analysis,ICA)與小波方法的特征提取方法,ICA方法能夠去除各通道間的數(shù)據(jù)冗余,去除明顯噪聲的同時(shí)對有效編碼信息最大程度的保留,之后利用時(shí)頻特性特較好的小波方法進(jìn)行特征提取,有效提取了運(yùn)動(dòng)轉(zhuǎn)向發(fā)生時(shí)局部場電位的特征。3.完成了鴿子運(yùn)動(dòng)轉(zhuǎn)向行為誘導(dǎo)訓(xùn)練及神經(jīng)信號(hào)采集實(shí)驗(yàn),利用本文的特征提取方法對局部場電位信號(hào)進(jìn)行特征提取,并利用k近鄰方法進(jìn)行分類,正確率最高達(dá)到92.35%。又進(jìn)一步對不同通道,單個(gè)特征,及不同時(shí)間窗提取的特征進(jìn)行分類并統(tǒng)計(jì)正確率,探究比較好的解碼特征形式。
[Abstract]:The study of neural signal decoding of motor behavior is an important part of brain-computer interface research. Decoding biological motion intention has important theoretical and practical application value. The signals recorded by microelectrode array include spike signal (spike) and local field potential signal (Local field potentials,LFP). Some achievements have been made in the study of motion decoding of spike signal, but the quality of spike signal has declined with the time of electrode implantation. The local field potential has the characteristics of long-term decoding stability, and gradually attracted the attention of researchers. However, the structure of the brain is complex, the execution of a certain motor behavior may be affected by multiple brain regions, the duration of the motor intention in the brain is very short, and the local field potential collected is a non-stationary signal. Noise signals will inevitably be introduced in the process of recording, which will hinder the efficient extraction of decoding features. When decoding the brain information, the key is to determine the time and frequency window of the effective coding information, and extract the features that are beneficial to the decoding. In this paper, pigeons were studied. The LFP signals in NCL (Nidopallium Caudolaterale) brain region were collected when pigeons turned left, straight and right, combined with animal behavior method and neural signal microelectrode array detection technique. The feature extraction method of LFP signal is analyzed, and the feature change of LFP signal when pigeon is moving and turning is discussed. The extracted feature is classified by k-nearest neighbor (k-Nearest Neighbor,kNN) method, and its motion direction is predicted. The main work is as follows: 1. The generation process of local field potential signal, the characteristics of local field potential signal and related noise characteristics are analyzed. On this basis, the advantages and disadvantages of common processing methods in LFP signal denoising are compared, as well as the common time domain. The advantages and disadvantages of time-frequency domain method in feature extraction. A new feature extraction method based on Independent component Analysis (Independent component analysis,ICA) and wavelet method is proposed, which can remove the data redundancy between the channels, remove the obvious noise, and keep the effective coding information to the maximum extent. After that, the feature extraction is carried out by wavelet method, which is especially good in time-frequency characteristic, and the feature of local field potential at the time of motion turn is extracted effectively. 3. The training of pigeon movement steering behavior and the experiment of nerve signal acquisition were completed. The feature extraction method of this paper was used to extract the feature of local field potential signal, and the k-nearest neighbor method was used to classify the local field potential signal. The highest correct rate was 92.35%. Furthermore, the features extracted from different channels, single features and different time windows are classified and the correct rate is counted to explore the better decoding feature form.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN911.7

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