本文選題：腦電信號　切入點：單側(cè)準備電位　出處：《哈爾濱師范大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：認知神經(jīng)科學(xué)是探究人腦神經(jīng)機制的一門學(xué)科,已經(jīng)成為現(xiàn)代科學(xué)研究的熱門課題,時間相關(guān)電位(Event Related Potential, ERP)因其無創(chuàng)性和經(jīng)濟性而成為認知神經(jīng)科學(xué)研究的主要手段.單側(cè)準備電位是ERP的基本成分之一,它是指在隨意運動中,反應(yīng)效應(yīng)器方位所對應(yīng)的對側(cè)大腦皮層出現(xiàn)的準備電位(Lateralized readiness potential, LRP)可以顯示被試反應(yīng)的正確率,還可以對運動預(yù)備的程度進行定量,即運動預(yù)備的程度高,則LRP波幅也高.LRP研究的重點是啟動點和峰值的測量.LRP的啟動點是劃分反應(yīng)階段的重要的時程指標,LRP峰值是表征預(yù)備程度的指標.這兩個指標的測量都是在獲取LRP波形的基礎(chǔ)上進行,而現(xiàn)實中,LRP波形的低信噪比大大影響了啟動點和峰值的測量精度.傳統(tǒng)的啟動點測量方法有三類,基線漂移法,閾值法和分段回歸法,基線漂移法和閾值法只用到LRP的局部信息進行推斷,測量結(jié)果嚴重受到噪音的影響,相比之下,分段回歸法是基于LRP的整體信息進行測量,但是,分段回歸不能恰當(dāng)?shù)拿枋鯨RP的連續(xù)過程.本文提出了基于指數(shù)擬合的啟動點測量方法,該方法能夠克服噪音對測量的影響,又能體現(xiàn)LRP的連續(xù)過程.真實數(shù)據(jù)分析結(jié)果顯示,本方法大大提高了測量精度.本文針對峰值提出基于滑動平均的測量方法.這一方法測量的結(jié)果更準確穩(wěn)定.這兩個方法的提出不僅對于不同試驗條件下啟動點差異的測量提供了更有效的途徑,對單個試驗條件下啟動點的精準測量也是有效的.
[Abstract]:Cognitive neuroscience is a subject that explores the mechanism of human brain nerve, and has become a hot topic in modern scientific research. Event Related potential (ERP) has become the main method of cognitive neuroscience research because of its noninvasive and economical properties. Unilateral preparatory potential is one of the basic components of ERP, which refers to random exercise. The contralateral readiness potential (LRP) corresponding to the orientation of the response effector can show the correct rate of the reaction, and can also quantify the degree of motor preparation, that is, the degree of motor preparation is high. Then the research of LRP amplitude is also high. The emphasis of the research is that the starting point and the measurement of peak value. The starting point of LRP is an important time-history index to divide the reaction stage. The peak value of LRP is an indicator of the degree of preparation. The measurement of these two indexes is to obtain the LRP. Based on the waveform, In reality, the low signal-to-noise ratio (SNR) of LRP waveform greatly affects the measurement accuracy of starting point and peak value. There are three kinds of traditional starting point measurement methods: baseline drift method, threshold method and piecewise regression method. The baseline drift method and threshold method only use the local information of LRP to infer, and the measured results are seriously affected by noise. In contrast, the piecewise regression method is based on the whole information of LRP, but, Piecewise regression can not properly describe the continuous process of LRP. In this paper, a starting point measurement method based on exponential fitting is proposed, which can overcome the influence of noise on the measurement and reflect the continuous process of LRP. The results of real data analysis show that, This method has greatly improved the accuracy of measurement. In this paper, a new measuring method based on moving average for peak value is proposed. The results of this method are more accurate and stable. These two methods are not only for the starting point under different test conditions. The measurement of differences provides a more effective way, Accurate measurement of the starting point under a single test condition is also effective.
【學(xué)位授予單位】：哈爾濱師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：O213

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