本文選題：注意力腦電 + 去趨勢互相關(guān)分析�。� 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：隨著社會的發(fā)展,腦力勞動(dòng)占據(jù)人類活動(dòng)的比重逐漸增高,注意力是否集中直接影響到了工作效率。因此,通過分析不同注意任務(wù)中的腦電信號,并進(jìn)行分類具有重要意義。本文正是基于這一目標(biāo),通過對不同注意任務(wù)(冥想狀態(tài)和放松狀態(tài))中的腦電信號進(jìn)行低通濾波。然后提取包含低頻波段的腦電信號。最后依次使用去趨勢互相關(guān)算法(Detrended Cross-Correlation Analysis,簡稱DCCA)和多重分形去趨勢互相關(guān)分析算法(Multifractal Detrended Cross-Correlation Analysis,簡稱MF-DCCA)分析不同注意任務(wù)中的低頻波段腦電信號。以達(dá)到準(zhǔn)確判斷大腦注意力活動(dòng)狀態(tài)的目的。本文主要研究內(nèi)容有以下三點(diǎn):一、基于去趨勢互相關(guān)的注意力腦電信號分析。使用去趨勢互相關(guān)算法分別對受試者冥想狀態(tài)和放松狀態(tài)時(shí)的腦電信號進(jìn)行分析計(jì)算,得到各自去趨勢互相關(guān)指數(shù)。通過分析結(jié)果,我們發(fā)現(xiàn)在不同注意力狀態(tài),腦電信號的去趨勢互相關(guān)指數(shù)有明顯區(qū)別。注意力集中時(shí),腦電信號的去趨勢互相關(guān)指數(shù)更接近常數(shù)1,因此,注意力集中,腦電信號的長程相關(guān)性更強(qiáng)。因此可以通過觀察腦電信號的去趨勢互相關(guān)指數(shù)的變化,判斷觀察對象的注意力集中狀態(tài)。這對輔助腦疾病康復(fù)治療具有重要意義。二、基于多重分形去趨勢互相關(guān)的注意力腦電信號分析。研究了另一種腦電信號分析算法,即多重分形去趨勢互相關(guān)分析算法。這種算法通過不同的參數(shù)和角度證明腦電信號的多重分形特性。通過分析得到以下結(jié)論:注意力集中時(shí),腦電信號的多重分形去趨勢互相關(guān)指數(shù)更接近常數(shù)1,所以腦電信號的長程相關(guān)性更強(qiáng)。三、基于Android與Java EE腦機(jī)接口系統(tǒng)實(shí)現(xiàn)。為了讓研究結(jié)果具有實(shí)際意義,本文通過Android移動(dòng)智能設(shè)備,腦電信號傳感器以及服務(wù)器端的Web應(yīng)用搭建了一套腦機(jī)接口。本系統(tǒng)可以實(shí)時(shí)采集、分析腦電信號,存儲腦電信號。本系統(tǒng)對日后更深入研究腦電信號具有重要意義。
[Abstract]:With the development of society, the proportion of mental labor in human activities increases gradually, and the concentration of attention directly affects the work efficiency. Therefore, it is important to analyze and classify EEG signals in different attention tasks. Based on this goal, the EEG signals in different attention tasks (meditative state and relaxation state) are filtered by low-pass filtering. Then the EEG signal containing low frequency band is extracted. In the end, Detrended Cross-Correlation Analysis (DCCA) and multifractal Detrended Cross-Correlation Analysis (MF-DCCA) are used to analyze the low frequency band EEG signals in different attention tasks. In order to accurately judge the state of brain attention activity. The main contents of this paper are as follows: first, attention EEG analysis based on detrend correlation. The detrend cross-correlation algorithm was used to analyze and calculate the EEG signals in the meditative state and relaxation state of the subjects, and their detrend cross-correlation indices were obtained. Through the analysis, we find that there are obvious differences in the detrend cross-correlation index of EEG in different attention states. When attention is concentrated, the detrend cross-correlation index of EEG signal is closer to constant 1, therefore, the long-term correlation of EEG signal is stronger when attention is concentrated. So we can judge the state of attention concentration by observing the change of the detrend cross-correlation index of EEG signal. It is of great significance to assist the rehabilitation treatment of brain diseases. Second, the analysis of attention EEG based on multifractal detrend correlation. In this paper, another EEG signal analysis algorithm, multifractal de-trend cross-correlation analysis algorithm, is studied. The multifractal characteristics of EEG signals are proved by different parameters and angles. The conclusion is as follows: when attention is concentrated, the multifractal detrend cross-correlation index of EEG signal is closer to constant 1, so the long range correlation of EEG signal is stronger. Third, based on Android and Java EE brain computer interface system implementation. In order to make the research results have practical significance, this paper builds a set of brain-computer interface through Android mobile intelligent device, EEG sensor and Web application on the server side. This system can collect, analyze and store EEG signals in real time. This system is of great significance for the further study of EEG in the future.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318;TN911.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 吳秀娟;;ASP、PHP和JSP在動(dòng)態(tài)網(wǎng)頁制作技術(shù)比較分析[J];數(shù)字技術(shù)與應(yīng)用;2016年10期

2 肖澤元;李廣利;寇猛;李國瑞;;風(fēng)險(xiǎn)感知水平對礦工注意力集中能力的影響研究[J];技術(shù)與創(chuàng)新管理;2016年05期

3 劉春瓊;劉萍;吳生虎;史凱;;基于DCCA方法分析氣候變化對四川省糧食產(chǎn)量的影響[J];中國農(nóng)業(yè)氣象;2016年01期

4 馬征;邱天爽;;視覺ERP腦機(jī)接口中實(shí)驗(yàn)范式的研究進(jìn)展[J];中國生物醫(yī)學(xué)工程學(xué)報(bào);2016年01期

5 苑瑩;莊新田;金秀;;中國股市的日內(nèi)效應(yīng)、長記憶性及多重分形性:基于價(jià)-量交叉相關(guān)性視角[J];系統(tǒng)管理學(xué)報(bào);2016年01期

6 胡葉容;;基于去趨勢波動(dòng)分析的腦電信號睡眠分期[J];生物醫(yī)學(xué)工程學(xué)進(jìn)展;2015年04期

7 王凱明;鐘寧;周海燕;;基于改進(jìn)功率譜熵的抑郁癥腦電信號活躍性研究[J];物理學(xué)報(bào);2014年17期

8 孟慶芳;陳珊珊;陳月輝;馮志全;;基于遞歸量化分析與支持向量機(jī)的癲癇腦電自動(dòng)檢測方法[J];物理學(xué)報(bào);2014年05期

9 王玉蘭;王俊;;睡眠腦電的去趨勢互相關(guān)分析[J];生物醫(yī)學(xué)工程學(xué)雜志;2014年01期

10 曾志堅(jiān);張倩倩;;基于MF-DCCA方法的證券市場間交叉相關(guān)性研究[J];財(cái)經(jīng)理論與實(shí)踐;2013年06期

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