本文關(guān)鍵詞：基于EEG的運(yùn)動(dòng)想象分類與識(shí)別算法及其在腦—機(jī)接口中的應(yīng)用　出處：《安徽大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 腦—機(jī)接口 運(yùn)動(dòng)想象 共同空間模式 隱馬爾科夫模型 SWICA算法

【摘要】：基于腦電的腦—機(jī)接口(Brain-Computer Interface, BC1)技術(shù)作為一種新型人機(jī)交互手段,近年來已經(jīng)成為康復(fù)工程以及生物醫(yī)學(xué)工程等領(lǐng)域的研究熱點(diǎn)。腦一機(jī)接口是在人腦和外界環(huán)境之間建立直接通信,而不依賴外周神經(jīng)以及肌肉這種正常的輸出通道。對(duì)腦電數(shù)據(jù)的正確分類是決定腦—機(jī)接口性能的關(guān)鍵因素,因此研究基于腦電的分類識(shí)別算法具有重要的現(xiàn)實(shí)意義。 本文以基于運(yùn)動(dòng)想象的腦—機(jī)接口作為研究對(duì)象,對(duì)基于運(yùn)動(dòng)想象的腦電信號(hào)的特征提取方法和分類識(shí)別算法進(jìn)行了系統(tǒng)的研究,并實(shí)現(xiàn)了以基于滑動(dòng)窗的ICA(Sliding Window ICA, SWICA)為核心算法的在線BCI系統(tǒng)。論文的主要內(nèi)容如下： 1.設(shè)計(jì)了基于運(yùn)動(dòng)想象的BCI實(shí)驗(yàn)范式,采集了較豐富的左右手運(yùn)動(dòng)想象腦電數(shù)據(jù)；結(jié)合已有的標(biāo)準(zhǔn)EEG數(shù)據(jù),建立了用于本文離線BCl分析的實(shí)測(cè)腦電信號(hào)數(shù)據(jù)庫。 2.研究了基于后驗(yàn)概率的支持向量機(jī)(Posteriori Probability Support Vector Machine, PPSVM)和隱馬爾科夫模型(Hidden Markov Models, HMM)等模式識(shí)別方法,并結(jié)合腦電模式分類問題,進(jìn)行了不同分類方法的性能比較。并用基于后驗(yàn)概率的支持向量機(jī)檢測(cè)出了運(yùn)動(dòng)想象過程中的“休息”狀態(tài),為實(shí)現(xiàn)運(yùn)動(dòng)想象在線控制系統(tǒng)創(chuàng)造了良好的條件。 3.研究并實(shí)現(xiàn)了基于能量、共同空間模式(Common Spatical Pattern, CSP)和SWICA算法的EEG特征提取方法。重點(diǎn)研究了基于SWICA的信號(hào)包絡(luò)檢測(cè)新方法,并將該算法應(yīng)用于腦電mu節(jié)律的動(dòng)態(tài)特性分析和運(yùn)動(dòng)想象分類,得到了較高的運(yùn)動(dòng)想象分類識(shí)別率。 4.設(shè)計(jì)并實(shí)現(xiàn)了基于SWICA算法的在線BCI系統(tǒng),實(shí)驗(yàn)證明,該系統(tǒng)可以實(shí)時(shí)在線的識(shí)別出左右手運(yùn)動(dòng)想象,識(shí)別率最高可達(dá)92.7%。
[Abstract]:EEG based brain computer interface (Brain-Computer Interface BC1) technology as a new means of human-computer interaction, in recent years has become a hotspot in the research fields of rehabilitation engineering and biomedical engineering. A brain computer interface is a direct communication between human and environment, and not rely on peripheral nerves and muscles of the normal output channel. The correct classification of EEG data is a key factor determining the BCI performance, so the research on classification and recognition algorithm based on EEG has important practical significance.
Based on motor imagery based on brain computer interface as the research object, the feature extraction method of EEG of motor imagery classification and recognition algorithm based on systematic research, and in order to realize the sliding window based on ICA (Sliding Window ICA, SWICA) online BCI system as the core of the algorithm. The main contents of this paper the following:
1., we designed a BCI experimental paradigm based on motor imagery, and collected abundant left and right hand motor imagery EEG data. Combined with the existing standard EEG data, we set up a database of measured EEG for off-line BCl analysis.
2. support vector machine is studied based on the posterior probability (Posteriori Probability Support Vector Machine, PPSVM) and hidden Markov model (Hidden Markov Models, HMM) and other methods of pattern recognition, and combined with EEG pattern classification problems, compare the performance of different classification methods. The support vector machine and detection based on posterior probability the movement of imagination in the process of "rest" state, to create good conditions for online motor imagery control system.
3. research and Implementation Based on energy, common spatial pattern (Common Spatical, Pattern, CSP) EEG feature extraction and SWICA algorithm. The method focuses on the new method of signal detection based on SWICA, and the classification of imagination dynamic characteristics analysis and motion of the algorithm is applied to mu rhythm of EEG, get high motor imagery classification and recognition rate.
4., we design and implement an online BCI system based on SWICA algorithm. Experiments show that the system can identify the left and right hand motor imagery online and online, and the recognition rate is up to 92.7%..

【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.41;TP334.7

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 楊雅;基于貝葉斯理論的運(yùn)動(dòng)想象信號(hào)分析方法研究[D];華南理工大學(xué);2013年

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本文編號(hào)：1412435