發(fā)布時(shí)間：2018-02-06 00:48

  本文關(guān)鍵詞： 腦機(jī)接口 穩(wěn)態(tài)視覺(jué)誘發(fā)電位 譜校正 點(diǎn)通濾波 全相位FFT　出處：《天津大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)科學(xué)、信號(hào)處理技術(shù)的飛速發(fā)展,以及殘疾患者的生活質(zhì)量提高意識(shí)不斷加強(qiáng),近年來(lái)在臨床治療中涌現(xiàn)出大量使用腦機(jī)接口(BCI)的康復(fù)輔助手段。其中基于穩(wěn)態(tài)視覺(jué)誘發(fā)電位(SSVEP)的BCI因具有非侵犯性、系統(tǒng)配置簡(jiǎn)單及其高信息轉(zhuǎn)化率的優(yōu)勢(shì),故被選作大腦命令的良好載體。而衡量SSVEP-BCI系統(tǒng)性能的標(biāo)準(zhǔn)之一就是該系統(tǒng)可產(chǎn)生的命令數(shù)(即目標(biāo)激勵(lì)塊數(shù)目),命令數(shù)越多,對(duì)應(yīng)的執(zhí)行動(dòng)作也越多,系統(tǒng)也越完善。目前最常用的SSVEP命令識(shí)別方法是通過(guò)提取腦電信號(hào)的頻率信息來(lái)實(shí)現(xiàn)的,而對(duì)于采用液晶顯示器(LCD)激勵(lì)產(chǎn)生的SSVEP信號(hào),由于其激勵(lì)頻率是通過(guò)對(duì)LCD的刷新頻率整數(shù)分頻得到的,因而其激勵(lì)頻率數(shù)目受到限制,而且還不得不排除一些因非同步采樣而無(wú)法直接準(zhǔn)確檢測(cè)到的激勵(lì)頻率。此外,因電極采集的腦電信號(hào)非常微弱及存在偽跡成分的干擾,都會(huì)影響檢測(cè)精度,需要引入新的信號(hào)處理措施給予改善。為提高所容許的窄頻帶內(nèi)可檢測(cè)的命令數(shù)目,本文提出結(jié)合頻率與相位差混合編碼的BCI設(shè)計(jì),在編碼環(huán)節(jié),通過(guò)賦予激勵(lì)塊以頻率與相位相結(jié)合的特征而增大了目標(biāo)激勵(lì)數(shù),有效地提高了可用頻帶的利用率。為提高目標(biāo)檢測(cè)精度,在解碼環(huán)節(jié),本文引入了頻譜校正理論、全相位點(diǎn)通濾波器設(shè)計(jì)理論、全相位FFT譜分析理論和模式分類(lèi)技術(shù)進(jìn)行頻率、相位信息提取。其中,借助頻譜校正,檢測(cè)出非同步采樣下SSVEP的頻率和相位值;借助全相位點(diǎn)通濾波,濾除了激勵(lì)頻率的帶外干擾,提高了信號(hào)質(zhì)量;利用全相位FFT譜分析的“相位不變性”和良好的抑制譜泄漏性能,進(jìn)一步抑制了臨近頻率干擾,提升了相位估計(jì)精度,降低了計(jì)算復(fù)雜度;最后,結(jié)合最近鄰分類(lèi)法,完成了基于相位特征的目標(biāo)識(shí)別。本文不但借助理論分析和仿真實(shí)驗(yàn)論證了以上方法在提取SSVEP頻率與相位特征的可行性,而且通過(guò)現(xiàn)場(chǎng)數(shù)據(jù)采集與后續(xù)實(shí)驗(yàn)處理,結(jié)果表明:綜合以上方法能夠解決SSVEP激勵(lì)信號(hào)在非同步采樣下引起的測(cè)不準(zhǔn)相位的問(wèn)題,從而大大放寬了對(duì)目標(biāo)激勵(lì)頻率的限制,增加了激勵(lì)目標(biāo)數(shù)目,具有很高的臨床利用價(jià)值。
[Abstract]:With the rapid development of computer science, signal processing technology, and the quality of life of patients with disabilities to improve awareness is constantly strengthened. In recent years, a large number of rehabilitation aids using brain-computer interface (BCI) have emerged in clinical treatment. Among them, BCI based on steady-state visual evoked potential (SSVEP) is non-invasive. The system configuration is simple and has the advantage of high information conversion rate. Therefore, it is chosen as a good carrier of brain commands. One of the criteria for measuring the performance of SSVEP-BCI system is the number of commands that can be generated by the system (that is, the number of target excitation blocks, the number of commands). The more corresponding actions, the more perfect the system. At present, the most commonly used SSVEP command recognition method is to extract the frequency information of EEG. For the SSVEP signal excited by liquid crystal display (LCD), the number of the excitation frequency is limited because the excitation frequency is obtained by the integer division of the refresh frequency of the LCD. Moreover, some excitation frequencies which can not be detected directly and accurately because of asynchronous sampling have to be eliminated. In addition, the detection accuracy will be affected by the very weak EEG signals collected by electrodes and the interference of artifacts. New signal processing measures need to be introduced to improve it. In order to increase the number of detectable commands in the narrow band, this paper proposes a hybrid BCI design combining frequency and phase difference coding in the coding link. In order to improve the target detection accuracy, the target excitation number is increased by giving the excitation block the characteristic of combining frequency and phase, and the efficiency of available frequency band is improved effectively. In this paper, the spectrum correction theory, the design theory of all phase point pass filter, the all phase FFT spectrum analysis theory and the pattern classification technology are introduced to extract the frequency and phase information. The frequency and phase of SSVEP are detected under asynchronous sampling. With the help of all-phase point-pass filtering, the out-of-band interference of excitation frequency is filtered, and the signal quality is improved. Using the "phase invariance" of all phase FFT spectrum analysis and good spectral leakage suppression performance, the near frequency interference is further suppressed, the accuracy of phase estimation is improved, and the computational complexity is reduced. Finally, combined with the nearest neighbor classification, the target recognition based on phase features is completed. This paper not only uses theoretical analysis and simulation experiments to demonstrate the feasibility of the above methods in extracting SSVEP frequency and phase features. And through the field data acquisition and subsequent experimental processing, the results show that the synthesis of the above method can solve the problem of uncertain phase caused by asynchronous sampling of SSVEP excitation signal. Therefore, the limit of motivation frequency is greatly relaxed, and the number of incentive goals is increased, which is of high clinical value.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：R318;TN911.7

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