【摘要】：腦-機(jī)接口(Brain-Computer Interface, BCI)技術(shù)不依賴于大腦的外周神經(jīng)系統(tǒng)與肌肉組織為大腦與外界開(kāi)辟了一條新的特殊通道。它可以不需要語(yǔ)言或肢體動(dòng)作而直接通過(guò)腦電來(lái)完成對(duì)外部環(huán)境及裝置的控制。當(dāng)前的腦-機(jī)接口研究正處于發(fā)展階段,如何快速準(zhǔn)確地識(shí)別腦電模式,是腦-機(jī)接口領(lǐng)域中的一個(gè)熱點(diǎn)問(wèn)題。 本文展開(kāi)了基于P300的腦機(jī)接口系統(tǒng)的研究。P300是一種小概率誘發(fā)電位,常用于構(gòu)建腦-機(jī)接口系統(tǒng)的腦電信號(hào)。P300響應(yīng)是在目標(biāo)刺激300ms左右出現(xiàn)的一個(gè)正向波�；赑300的腦-機(jī)接口系統(tǒng),受試者不需要進(jìn)行特別的訓(xùn)練就能達(dá)到較好的效果。 本文工作是對(duì)P300實(shí)驗(yàn)范式和分類識(shí)別的研究。主要做了以下幾項(xiàng)工作： (1)選擇貝葉斯線性判別分析(BLDA),通過(guò)處理離線階段和在線階段的實(shí)驗(yàn)數(shù)據(jù),研究隨機(jī)激勵(lì)和非隨機(jī)激勵(lì)產(chǎn)生的P300信號(hào)的特點(diǎn),得出P300隨機(jī)激勵(lì)誘發(fā)產(chǎn)生的P300比非隨機(jī)激勵(lì)更明顯、易于識(shí)別。 (2)分別用貝葉斯線性判別分析(BLDA)、線性判別分析(LDA4)與支持向量機(jī)(SVM)作為分類識(shí)別算法,對(duì)離線階段隨機(jī)激勵(lì)產(chǎn)生的P300信號(hào)分類結(jié)果進(jìn)行比較。基于BLDA算法的分類精度最優(yōu)；LDA4算法下也得到比較好的分類精度,相對(duì)BLDA的效果次之；SVM下的分類精度相對(duì)比較差。且SVM識(shí)別所花費(fèi)比較長(zhǎng)的時(shí)間,BLDA所花費(fèi)時(shí)間最短,相對(duì)于SVM快很多。 (3)在腦電信號(hào)的消噪上,引進(jìn)了獨(dú)立分量分析(ICA)方法,通過(guò)Infomax ICA和FastICA兩種算法消除腦電信號(hào)中的眼電,以期得到噪聲較少的腦電信號(hào)。利用貝葉斯線性判別分析(BLDA)方法,來(lái)判別Infomax ICA和FastICA去噪效果,并且和未去噪的結(jié)果進(jìn)行比較,來(lái)驗(yàn)證去噪的有效性。
[Abstract]:Brain-computer interface (Brain-Computer Interface, BCI) technology does not depend on the peripheral nervous system and muscle tissue of the brain to open up a new special channel for the brain and the outside world. It can control the external environment and device directly through EEG without the need of language or limb movement. At present, the research of brain-computer interface is in the developing stage. How to recognize EEG pattern quickly and accurately is a hot issue in the field of brain-computer interface. In this paper, a P300-based brain-computer interface system is studied. P300 is a small probability evoked potential, which is often used to construct EEG signals of brain-computer interface system. P300 response is a positive wave around the target stimulation 300ms. P300-based brain-computer interface system, the subjects do not need special training to achieve better results. The work of this paper is to study the P300 experimental paradigm and classification recognition. The main work is as follows: (1) the Bayesian linear discriminant analysis (BLDA),) is selected to study the characteristics of P300 signals generated by random excitation and non-random excitation by processing the experimental data of off-line and on-line phases. It is concluded that P300 induced by random excitation is more obvious than non-random excitation and is easy to identify. (2) using Bayesian linear discriminant analysis (LDA4) (BLDA), linear discriminant analysis (LDA4) and support vector machine (SVM) as classification recognition algorithms, the classification results of P300 signals generated by off-line random excitation are compared. The classification accuracy based on BLDA algorithm is the best; the classification accuracy under LDA4 algorithm is better than that of BLDA; the classification accuracy under SVM is relatively poor. And SVM takes a long time to identify, and BLDA takes the shortest time, much faster than SVM. (3) Independent component analysis (ICA) is introduced to eliminate the noise of EEG signals. The two algorithms, Infomax ICA and FastICA, are used to eliminate the eye electricity in EEG signals, in order to get the EEG signals with less noise. The Bayesian linear discriminant analysis (BLDA) method is used to judge the denoising effect of Infomax ICA and FastICA, and the results are compared with those of non-denoising to verify the effectiveness of de-noising.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP334.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 程明,高上凱,張琳;基于腦電信號(hào)的腦—計(jì)算機(jī)接口[J];北京生物醫(yī)學(xué)工程;2000年02期

2 楊立才,李佰敏,李光林,賈磊;腦-機(jī)接口技術(shù)綜述[J];電子學(xué)報(bào);2005年07期

3 張宇;張建華;王行愚;金晶;;基于FastICA的P300電位快速提取方法[J];華東理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年05期

4 高諾;魯守銀;張運(yùn)楚;姚慶梅;;腦機(jī)接口技術(shù)的研究現(xiàn)狀及發(fā)展趨勢(shì)[J];機(jī)器人技術(shù)與應(yīng)用;2008年04期

5 李鋼;王蔚;張勝;;支持向量機(jī)在腦電信號(hào)分類中的應(yīng)用[J];計(jì)算機(jī)應(yīng)用;2006年06期

6 周衛(wèi)東;基于獨(dú)立分量分析的生理信號(hào)盲源分離(英文)[J];山東生物醫(yī)學(xué)工程;2002年02期

7 葛瑜;楊鋼;;腦機(jī)接口系統(tǒng)中EEG信號(hào)特征提取與分類[J];微計(jì)算機(jī)信息;2008年18期

8 吳正茂,孫云蓮;基于快速獨(dú)立分量分析的多維混合信號(hào)盲分離[J];武漢大學(xué)學(xué)報(bào)(工學(xué)版);2005年04期

9 李婷;邱天爽;;基于帶參考信號(hào)的ICA算法的腦電信號(hào)眨眼偽差的分離研究[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2006年03期

10 張宇;王行愚;張建華;金晶;;離散粒子群優(yōu)化-貝葉斯線性判別分析算法用于視覺(jué)事件相關(guān)電位P300的分類[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2010年01期

相關(guān)碩士學(xué)位論文 前1條

1 黃安湖;P300腦電誘發(fā)電位的分類識(shí)別及在腦機(jī)接口中的應(yīng)用[D];山東大學(xué);2008年

，

本文編號(hào)：2442695