發(fā)布時間：2018-06-07 14:37

  本文選題：腦電 + 神經(jīng)解碼�。� 參考：《天津大學》2012年碩士論文

【摘要】：運動能力的恢復對肢體活動能力受損的人群至關(guān)重要,近年來雖然針對運動障礙患者的診斷和康復實驗研究取得了巨大發(fā)展,但其總體療效卻并不令人滿意。近些年來將腦機接口技術(shù)融入到康復治療中的思想引起了越來越多的研究者的興趣。與傳統(tǒng)方法相比,基于腦機接口技術(shù)的康復器械可以根據(jù)患者的實際情況,通過神經(jīng)解碼產(chǎn)生的動作進行相關(guān)訓練,使患者動作意圖和肢體實際動作相互配合協(xié)調(diào),達到最佳的訓練效果。 本文首先根據(jù)具體的實驗需求設(shè)計并搭建了由腦電采集設(shè)備、運動采集設(shè)備和指示裝置組成的實驗系統(tǒng)。依據(jù)布置方便、實時準確的原則設(shè)計了以LM3S9B96為主控芯片、TFT顯示屏作指令輸出的指示裝置,并編寫了主控單元對顯示屏的控制程序。 數(shù)據(jù)處理階段首先對采集得到的原始信號進行了預處理,預處理包括數(shù)據(jù)濾波和腦電數(shù)據(jù)的分割。對于腦電信號的特征提取采用了功率估計、AR模型參數(shù)和小波系數(shù)提取。利用提取出的腦電特征參數(shù)以運動方向為標簽使用支持向量機進行了腦電數(shù)據(jù)的分類研究,對于三類運動的區(qū)分率最高為55.56%。由于支持向量機的參數(shù)對結(jié)果影響較大并難以規(guī)律性確定。本文隨后使用了粒子群優(yōu)化算法對支持向量機進行了參數(shù)優(yōu)化,并討論了優(yōu)化算法的過程和結(jié)果。由優(yōu)化結(jié)果可看出PSO算法大大減少了計算所需時間,同時在一定程度上降低了分類的成功率。
[Abstract]:The recovery of motor ability is very important to the people with impaired physical activity. Although great progress has been made in the diagnosis and rehabilitation of patients with motor disorders in recent years, the overall effect is not satisfactory. In recent years, the idea of integrating brain computer interface technology into rehabilitation therapy has attracted more and more researchers' interest. Compared with the traditional methods, rehabilitation instruments based on BCI technology can be trained according to the actual situation of the patients and the actions produced by the neural decoding, so that the intention of the patients and the actual movements of the limbs can be coordinated with each other. To achieve the best training results. This paper first designs and builds an experimental system composed of EEG acquisition equipment, motion acquisition equipment and indicator device according to the specific experimental requirements. According to the principle of convenient layout and real time accuracy, an indicator device with LM3S9B96 as the main control chip for instruction output is designed, and the control program of the main control unit to the display screen is compiled. In the data processing stage, the original signal is preprocessed, which includes data filtering and EEG data segmentation. The parameters of AR model and wavelet coefficients are extracted by power estimation. The classification of EEG data was carried out by using the extracted feature parameters of EEG using support vector machine (SVM). The highest discrimination rate of the three kinds of motion was 55.56%. Because the parameters of support vector machine (SVM) have a great influence on the result, it is difficult to determine regularly. In this paper, particle swarm optimization algorithm is used to optimize the parameters of support vector machine, and the process and result of the optimization algorithm are discussed. The optimization results show that the PSO algorithm greatly reduces the computation time and reduces the success rate of classification to a certain extent.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R318.0

【參考文獻】

相關(guān)期刊論文 前10條

1 王睿;;關(guān)于支持向量機參數(shù)選擇方法分析[J];重慶師范大學學報(自然科學版);2007年02期

2 程明,任宇鵬,高小榕,王廣志,季林紅,高上凱;腦電信號控制康復機器人的關(guān)鍵技術(shù)[J];機器人技術(shù)與應用;2003年04期

3 楊大鵬;姜力;趙京東;劉宏;;基于腦電信號的高智能假手控制[J];吉林大學學報(工學版);2008年05期

4 李愛國,覃征,鮑復民,賀升平;粒子群優(yōu)化算法[J];計算機工程與應用;2002年21期

5 王鵬,朱小燕;基于RBF核的SVM的模型選擇及其應用[J];計算機工程與應用;2003年24期

6 閆慶華;程兆剛;段云龍;;AR模型功率譜估計及Matlab實現(xiàn)[J];計算機與數(shù)字工程;2010年04期

7 周馳,高海兵,高亮,章萬國;粒子群優(yōu)化算法[J];計算機應用研究;2003年12期

8 任洪娥;霍滿冬;;基于PSO優(yōu)化的SVM預測應用研究[J];計算機應用研究;2009年03期

9 李慶玲;孫立寧;杜志江;;上肢康復機器人發(fā)展現(xiàn)狀的分析與研究[J];機械設(shè)計;2008年09期

10 張琳琳;陳明皓;;小波理論在醫(yī)學影像診斷中的應用[J];林區(qū)教學;2009年06期

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本文編號：1991527