【摘要】：在頭皮采集得到的腦電信號(hào)（Electroencephalogram，EEG）是腦細(xì)胞電生理活動(dòng)的整體反映，與人的意識(shí)活動(dòng)狀態(tài)相關(guān)，只要對(duì)腦電信號(hào)進(jìn)行分析，就可以識(shí)別出不同的意識(shí)活動(dòng)，從而形成一種不依賴于大腦外周神經(jīng)與肌肉正常輸出通道的通訊控制系統(tǒng)，即腦-機(jī)接口（Brain-Computer Interface，BCI）。運(yùn)動(dòng)想象是指只想象肢體運(yùn)動(dòng)而沒有進(jìn)行實(shí)際的肢體動(dòng)作，運(yùn)動(dòng)想象產(chǎn)生的腦電信號(hào)具有事件相關(guān)同步（event-related synchronization,ERS）和去同步（event-related desynchronization, ERD）特征，基于它的腦-機(jī)接口系統(tǒng)具有使用者不易疲勞、不依賴外界刺激器、適用人群廣、更符合使用習(xí)慣的優(yōu)點(diǎn)而備受關(guān)注，是研究熱點(diǎn)之一。 雖然運(yùn)動(dòng)想象腦電信號(hào)受到廣泛關(guān)注，但目前仍然存在很多急需解決的關(guān)鍵問題，如：基于左右手、腳、舌的四類運(yùn)動(dòng)想象研究仍停留在離線分析階段，在線效果還遠(yuǎn)達(dá)不到實(shí)際要求�；谧笥沂值膬深愡\(yùn)動(dòng)想象雖然有較好的在線效果，但產(chǎn)生的控制命令十分有限，而且屬于同步工作模式，，使用者無(wú)法完全自主控制。因此，本文主要針對(duì)基于運(yùn)動(dòng)想象的在線BCI系統(tǒng)如何進(jìn)一步提高精度和速度、增加控制自由度和實(shí)現(xiàn)異步工作進(jìn)行研究。 本文對(duì)四類運(yùn)動(dòng)想象腦電信號(hào)的采集、處理和異步腦-機(jī)接口系統(tǒng)的設(shè)計(jì)進(jìn)行了深入研究。采集部分對(duì)電極的安放位置、導(dǎo)聯(lián)方式以及采集實(shí)驗(yàn)的具體設(shè)計(jì)流程進(jìn)行了闡述，設(shè)計(jì)并實(shí)現(xiàn)了四類運(yùn)動(dòng)想象腦電信號(hào)的采集；預(yù)處理部分采用獨(dú)立分量分析和FIR數(shù)字濾波器分別去除眼電、肌電等干擾，通過(guò)比較濾波前后的小波時(shí)頻圖，對(duì)濾波效果進(jìn)行了分析；特征提取部分選用了功率譜估計(jì)、小波包分解和希爾伯特黃變換三種算法提取運(yùn)動(dòng)想象腦電信號(hào)的特征向量，并基于距離準(zhǔn)則對(duì)特征向量進(jìn)一步簡(jiǎn)化，得到最優(yōu)特征向量；模式識(shí)別部分采用一對(duì)一法構(gòu)建多分類支持向量機(jī)，并利用遺傳算法對(duì)其參數(shù)進(jìn)行優(yōu)化，通過(guò)對(duì)運(yùn)動(dòng)想象腦電信號(hào)的特征進(jìn)行分類實(shí)驗(yàn)，比較優(yōu)缺點(diǎn)，選擇出了較為理想的特征提取算法，為實(shí)時(shí)在線BCI系統(tǒng)分類器的選擇提供了依據(jù)；最后，結(jié)合Alpha波和運(yùn)動(dòng)想象兩種腦電信號(hào)的優(yōu)勢(shì)，設(shè)計(jì)控制策略，在LabVIEW平臺(tái)上建立了異步腦-機(jī)接口系統(tǒng)，實(shí)現(xiàn)了光標(biāo)的控制和網(wǎng)頁(yè)瀏覽功能。
[Abstract]:Electroencephalograms (EEG) collected on the scalp are an integral reflection of the electrophysiological activity of the brain cells, which is related to the state of human consciousness activity. As long as the EEG signal is analyzed, different conscious activities can be identified. Thus, a communication control system, Brain-Computer Interface (BCI), is formed, which is independent of the normal output channels of peripheral nerve and muscle. Motion imagination refers to the movement of the limbs without actual body movements. The EEG generated by the motion imagination has the characteristics of event-related synchronization and event-related synchronization. The brain-computer interface system based on it has many advantages, such as the user is not easy to fatigue, does not rely on the external stimulator, is suitable for a large number of people, and is more in line with the usage habits, so it is one of the research hot spots. Although the electroencephalogram (EEG) signal of motion imagination has received extensive attention, there are still many key problems that need to be solved. For example, the study of motion imagination based on left and right hand, foot and tongue is still at the stage of offline analysis. Online effect is still far from the actual requirements. Although the two kinds of motion imagination based on the left and the right hand have better online effect, the control commands produced are very limited, and they belong to the synchronous working mode, so the users can not control themselves completely. Therefore, this paper mainly focuses on how to improve the accuracy and speed of online BCI system based on motion imagination, increase the control degree of freedom and realize asynchronous work. In this paper, the acquisition, processing and design of asynchronous brain-computer interface system for four kinds of motion imagination EEG signals are studied. In the collection part, the location of the electrode, the lead mode and the specific design flow of the collection experiment are described, and the collection of four kinds of motion imaginary EEG signals is designed and realized. In the preprocessing part, independent component analysis (ICA) and Fir digital filter are used to remove EMG and EMG respectively. The filtering effect is analyzed by comparing the wavelet time-frequency images before and after filtering, and the power spectrum estimation is used in the feature extraction part. Wavelet packet decomposition and Hilbert-Huang transform are used to extract the eigenvector of the motion imaginary EEG signal, and the optimal eigenvector is obtained by further simplification of the eigenvector based on the distance criterion. In the part of pattern recognition, a one-to-one method is used to construct multi-classification support vector machine, and its parameters are optimized by genetic algorithm. The advantages and disadvantages are compared by classifying the characteristics of motion imaginary EEG signals. An ideal feature extraction algorithm is selected, which provides a basis for the selection of real-time online classifiers. Finally, combining the advantages of Alpha wave and motion imagination, a control strategy is designed. An asynchronous brain-computer interface system is established on LabVIEW platform, which realizes cursor control and web browsing.
【學(xué)位授予單位】：天津職業(yè)技術(shù)師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP334.7;TN911.7

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