發(fā)布時(shí)間：2018-05-04 21:09

  本文選題：腦機(jī)接口 + 運(yùn)動(dòng)想象��； 參考：《山東大學(xué)》2014年博士論文

【摘要】：探索大腦的奧秘是21世紀(jì)自然科學(xué)研究的重大挑戰(zhàn)。人腦是人體中最復(fù)雜的組織結(jié)構(gòu),是中樞神經(jīng)系統(tǒng)的最高級(jí)部分。腦部神經(jīng)元電活動(dòng)產(chǎn)生的腦電信號(hào)能夠反映大腦不同狀態(tài)的信息,因此,對(duì)腦電信號(hào)的研究是腦科學(xué)研究領(lǐng)域的重要組成部分。腦機(jī)接口(Brain Computer Interface, BCI)系統(tǒng)通過對(duì)腦電信號(hào)的分析和處理,提供用戶與外界設(shè)備通信和控制的信道,是一種新的人機(jī)交互方式。BCI系統(tǒng)涉及計(jì)算機(jī)通信與控制、生物醫(yī)學(xué)工程和康復(fù)醫(yī)學(xué)等領(lǐng)域,已經(jīng)成為交叉學(xué)科的熱點(diǎn)課題。 基于運(yùn)動(dòng)想象的BCI系統(tǒng)主要是將運(yùn)動(dòng)想象激發(fā)大腦運(yùn)動(dòng)皮層腦電節(jié)律變化的腦電信號(hào)作為輸入,通過信號(hào)處理部分判斷運(yùn)動(dòng)想象種類,然后由計(jì)算機(jī)將運(yùn)動(dòng)想象種類翻譯成控制命令,最終可以實(shí)現(xiàn)人腦與外部設(shè)備的通信及控制功能。信號(hào)處理作為基于運(yùn)動(dòng)想象的BCI系統(tǒng)的核心部分,主要包括預(yù)處理、信道選擇、特征提取和分類識(shí)別等主要步驟�；�于運(yùn)動(dòng)想象的BCI系統(tǒng)的性能關(guān)鍵在于對(duì)感知運(yùn)動(dòng)節(jié)律變化特征的準(zhǔn)確提取和對(duì)運(yùn)動(dòng)想象任務(wù)的正確分類。然而,由于腦電信號(hào)是一種微弱信號(hào),容易受到干擾,具有低信噪比、動(dòng)態(tài)性、瞬時(shí)性和非平穩(wěn)性等特點(diǎn),使得基于運(yùn)動(dòng)想象的BCI系統(tǒng)的發(fā)展和應(yīng)用面臨嚴(yán)峻的挑戰(zhàn)。如何有效地提取腦電信號(hào)特征,以及匹配最佳分類器是基于運(yùn)動(dòng)想象的BCI系統(tǒng)信號(hào)處理部分研究的重點(diǎn)。 本論文立足于腦電信號(hào)的預(yù)處理、信道選擇、特征提取和分類識(shí)別等四個(gè)方面開展算法研究。從時(shí)域、頻域、空域及非線性動(dòng)力學(xué)領(lǐng)域?qū)δX電信號(hào)進(jìn)行分析和處理,提取腦電特征并匹配最佳分類器,同時(shí)設(shè)計(jì)信道選擇算法以降低算法復(fù)雜度。本論文提出了幾種有效的基于運(yùn)動(dòng)想象的BCI系統(tǒng)特征提取和分類識(shí)別算法。采用國(guó)際標(biāo)準(zhǔn)的BCI競(jìng)賽數(shù)據(jù)庫驗(yàn)證算法有效性,本論文主要包括以下幾項(xiàng)貢獻(xiàn)和創(chuàng)新點(diǎn)： 1.在時(shí)頻域?qū)δX電信號(hào)進(jìn)行分析和處理,提出了一種基于改進(jìn)S變換的二類運(yùn)動(dòng)想象任務(wù)的特征提取和分類識(shí)別算法。該算法將改進(jìn)S變換引入腦皮層電圖(Electrocorticography,ECoG)信號(hào)的特征提取中,通過優(yōu)化兩個(gè)自適應(yīng)參數(shù)來調(diào)節(jié)可變窗口尺寸大小,從而得到一個(gè)最優(yōu)的頻率獨(dú)立窗口,可以準(zhǔn)確定位包含感知運(yùn)動(dòng)節(jié)律變化的腦電信號(hào)的時(shí)頻信息,通過計(jì)算經(jīng)過改進(jìn)S變換之后的ECoG信號(hào)的功率譜密度,最終得到腦電信號(hào)時(shí)頻范圍內(nèi)的局部功率表示。與S變換相比,改進(jìn)S變換具備更好的能量集中性；與其他時(shí)頻方法相比,基于改進(jìn)S變換的特征可以獲得更好的時(shí)頻分布表示和更高分辨率的頻譜密度函數(shù)；與其他分類器相比,將改進(jìn)S變換提取的特征與基于普通最小二乘回歸的梯度Boosting分類器相結(jié)合,可以得到最好的分類效果；設(shè)計(jì)的信道選擇算法能夠大幅度降低算法復(fù)雜度,有效提升BCI系統(tǒng)性能。實(shí)驗(yàn)結(jié)果證明,所提出的算法可以得到較好的分類效果。 2.在時(shí)空域?qū)δX電信號(hào)進(jìn)行分析和處理,提出了一種基于局部二值模式和自回歸模型相結(jié)合的特征提取和分類識(shí)別算法。該算法將廣泛應(yīng)用于圖像紋理分析的局部二值模式算子和自回歸模型構(gòu)成的組合特征應(yīng)用到一維腦電信號(hào)的分析中,并結(jié)合梯度Boosting分類器,對(duì)基于ECoG的運(yùn)動(dòng)想象任務(wù)進(jìn)行分類。利用旋轉(zhuǎn)不變的局部二值模式算子的直方圖分布和Burg算法所估計(jì)的二階自回歸模型系數(shù)構(gòu)成組合特征,在多個(gè)量化的角度和多分辨率基礎(chǔ)上分析腦電信號(hào),實(shí)現(xiàn)從時(shí)域和空域?qū)δX電信號(hào)感知運(yùn)動(dòng)節(jié)律變化的描述,反映腦電信號(hào)在運(yùn)動(dòng)想象中感知運(yùn)動(dòng)節(jié)律變化。該算法采用國(guó)際標(biāo)準(zhǔn)BCI競(jìng)賽數(shù)據(jù)庫進(jìn)行算法驗(yàn)證,實(shí)驗(yàn)結(jié)果證明,與其他幾種特征相比,該算法中的組合特征具備更高的分類準(zhǔn)確率,可以更好地描述基于運(yùn)動(dòng)想象的腦電信號(hào)；與其他分類器相比,將組合特征與基于普通最小二乘回歸的梯度Boosting分類器相結(jié)合,可以得到最好的分類效果。由于組合特征會(huì)導(dǎo)致特征向量維數(shù)的增加,因此,設(shè)計(jì)信道選擇算法來降低輸入的特征維數(shù)。實(shí)驗(yàn)結(jié)果證明該算法在保證分類準(zhǔn)確率的同時(shí)有效地降低了算法復(fù)雜度。 3.以非線性動(dòng)力學(xué)為基礎(chǔ),將分形幾何理論應(yīng)用到腦電信號(hào)的特征提取中,提出了一種分形特征和局部二值模式相結(jié)合的特征提取和分類識(shí)別算法。該算法引入廣泛應(yīng)用于灰度圖像計(jì)算的毯子維覆蓋技術(shù),通過計(jì)算腦電信號(hào)中不同覆蓋層的毯子維,得到相應(yīng)的分形截距和缺項(xiàng)。該算法將分形截距、缺項(xiàng)和空域提取的局部二值模式算子進(jìn)行組合來描述ECoG信號(hào)。該組合特征在多分辨率和多角度條件下分析腦電信號(hào),可以度量腦電信號(hào)的復(fù)雜度,同時(shí)反映其幅度變化快慢。與其他特征相比,該組合特征可以得到更好的分類效果,能夠更加完整和準(zhǔn)確地定位腦電信號(hào)中運(yùn)動(dòng)想象節(jié)律變化信息。信道選擇算法有效緩和組合特征導(dǎo)致的特征向量維數(shù)增加問題,從而降低運(yùn)算量。實(shí)驗(yàn)結(jié)果證明,該算法可以得到理想的分類效果,同時(shí)能夠獲取分類準(zhǔn)確率和算法復(fù)雜度之間更好地折中。 本文的研究工作有助于進(jìn)一步推動(dòng)基于運(yùn)動(dòng)想象的BCI系統(tǒng)在技術(shù)理論、算法和實(shí)際應(yīng)用中的研究。對(duì)于腦電信號(hào)的時(shí)域、頻域、空域和非線性動(dòng)力學(xué)分析在基于運(yùn)動(dòng)想象的BCI系統(tǒng)中的應(yīng)用起到了積極的推進(jìn)作用。
[Abstract]:The exploration of the mysteries of the brain is a major challenge in the study of Natural Science in the twenty-first Century. The human brain is the most complex structure in the human body and the most advanced part of the central nervous system. The EEG signals produced by the electrical activity of the brain neurons reflect the information of different states of the brain. Therefore, the study of brain electrical signals is an important part of the field of brain science. Brain Computer Interface (BCI) system provides channels for communication and control between users and external devices through the analysis and processing of EEG signals. It is a new human-computer interaction mode which has become a cross discipline in the fields of computer communication and control, biomedical engineering and rehabilitation medicine, and so on. Hot topics.
The BCI system based on motion imagination mainly uses the motion imagination to stimulate the EEG signals in the brain's motor cortex, which can be used as input to judge the kind of motion imagination through the signal processing section, and then translates the kind of motion imagination into control commands by the computer, and can finally realize the communication and control functions of the human brain and the external equipment. Signal processing is the core part of the BCI system based on motion imagination, mainly including preprocessing, channel selection, feature extraction and classification recognition. The performance key of BCI system based on motion imagination lies in the accurate extraction of the variation characteristics of perceptual motion rhythm and the correct classification of the task of motion imagination. However, because of the brain The electrical signal is a weak signal, easy to be disturbed, with low signal to noise ratio, dynamic, instantaneous and non-stationary, which makes the development and application of the BCI system based on motion imagination face severe challenges. How to effectively extract the features of the brain signal and match the best classifier is based on the BCI system signal of motion imagination The focus of the research.
This thesis is based on four aspects of EEG signal preprocessing, channel selection, feature extraction and classification recognition. The EEG signals are analyzed and processed in the domain of time domain, frequency domain, space domain and nonlinear dynamics, the EEG features are extracted and the best sorter is matched, and the channel selection algorithm is designed to reduce the complexity of the algorithm. In this paper, several effective algorithm for feature extraction and classification of BCI system based on motion imagination are proposed. The validity of the algorithm is verified by the international standard BCI competition database. The main contributions and innovations of this paper are as follows:
1. in the analysis and processing of the EEG in time and frequency domain, a feature extraction and classification recognition algorithm for two kinds of motion imaginary tasks based on improved S transform is proposed. This algorithm introduces the improved S transform to the feature extraction of Electrocorticography (ECoG) signal and adjusts the variable window by optimizing the two adaptive parameters. In order to obtain an optimal frequency independent window, it can accurately locate the time frequency information of the EEG signal including the perceptive motion rhythm. By calculating the power spectrum density of the ECoG signal after the improved S transform, the local power expression in the time frequency range of the EEG is obtained. Compared with the S transformation, the improved S change is improved. Better energy concentration; compared with other time-frequency methods, better time-frequency distribution and higher resolution spectrum density functions can be obtained based on the features of improved S transform. Compared with other classifiers, the features extracted from the S transform are combined with the gradient Boosting classifier based on the ordinary least two multiplied regression. The best classification effect can be obtained; the designed channel selection algorithm can greatly reduce the complexity of the algorithm and effectively improve the performance of the BCI system. The experimental results show that the proposed algorithm can get a better classification effect.
2. based on the analysis and processing of the EEG in the spatio-temporal domain, a feature extraction and classification recognition algorithm based on the combination of local two value model and autoregressive model is proposed. This algorithm applies the combination features of local two value pattern operators and autoregressive models to the one dimension EEG signal. In addition, it combines the gradient Boosting classifier to classify the motion imaginary tasks based on ECoG. The histogram distribution of the rotationally invariant local two value mode operator and the two order autoregressive model coefficients estimated by the Burg algorithm constitute the combination features, and the EEG signals are analyzed on the basis of multiple quantization angles and multi-resolution. The time domain and the spatial domain describe the changes in the motion rhythm of the EEG, reflecting the perceptive motion rhythm of the EEG in motion imagination. The algorithm is verified by the international standard BCI competition database. The experimental results prove that the combination features of the algorithm have a higher classification accuracy compared with the other features. To better describe the EEG signals based on motion imagination; compared with other classifiers, combining the combined features with the gradient Boosting classifier based on ordinary least squares regression, the best classification effect can be obtained. As the combination feature will cause the increase of the dimension of the feature vector, the channel selection algorithm is designed to reduce the input. Experimental results show that the algorithm can effectively reduce the complexity of the algorithm while ensuring the accuracy of classification.
3. on the basis of nonlinear dynamics, the fractal geometry theory is applied to the feature extraction of EEG signals. A feature extraction and classification recognition algorithm combined with fractal feature and local two value model is proposed. The algorithm introduces the blanket dimension covering technology widely used in gray image calculation, and calculates the different coverage of the brain electrical signals. The blanket dimension of the cover gets the corresponding fractal intercept and the missing item. The algorithm combines the fractal intercept, the missing term and the local two value mode operator extracted from the space to describe the ECoG signal. The combined feature can be used to analyze the EEG signal in the multi-resolution and multi angle conditions, and can measure the complexity of the EEG signal and reflect the rapid change of the amplitude. Slow. Compared with other features, the combination feature can get better classification results, and can more complete and accurately locate the motion picture of the EEG signal. Channel selection algorithm effectively mitigates the combination feature caused by the feature vector dimension increase, thus reducing the amount of operation. Experimental results show that the algorithm can be obtained. To achieve ideal classification results, we can get a better compromise between classification accuracy and algorithm complexity.
The research work in this paper will help to further promote the research of BCI system based on motion imagination in technical theory, algorithm and practical application. The time domain, frequency domain, space domain and nonlinear dynamic analysis of EEG have been actively promoted in the application of BCI system based on motion imagination.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7;TP334.8

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