本文選題：注意力腦電 + 去趨勢(shì)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(huì)的發(fā)展,腦力勞動(dòng)占據(jù)人類(lèi)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(guò)分析不同注意任務(wù)中的腦電信號(hào),并進(jìn)行分類(lèi)具有重要意義。本文正是基于這一目標(biāo),通過(guò)對(duì)不同注意任務(wù)(冥想狀態(tài)和放松狀態(tài))中的腦電信號(hào)進(jìn)行低通濾波。然后提取包含低頻波段的腦電信號(hào)。最后依次使用去趨勢(shì)互相關(guān)算法(Detrended Cross-Correlation Analysis,簡(jiǎn)稱(chēng)DCCA)和多重分形去趨勢(shì)互相關(guān)分析算法(Multifractal Detrended Cross-Correlation Analysis,簡(jiǎn)稱(chēng)MF-DCCA)分析不同注意任務(wù)中的低頻波段腦電信號(hào)。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?jī)?nèi)容有以下三點(diǎn):一、基于去趨勢(shì)互相關(guān)的注意力腦電信號(hào)分析。使用去趨勢(shì)互相關(guān)算法分別對(duì)受試者冥想狀態(tài)和放松狀態(tài)時(shí)的腦電信號(hào)進(jìn)行分析計(jì)算,得到各自去趨勢(shì)互相關(guān)指數(shù)。通過(guò)分析結(jié)果,我們發(fā)現(xiàn)在不同注意力狀態(tài),腦電信號(hào)的去趨勢(shì)互相關(guān)指數(shù)有明顯區(qū)別。注意力集中時(shí),腦電信號(hào)的去趨勢(shì)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(hào)的長(zhǎng)程相關(guān)性更強(qiáng)。因此可以通過(guò)觀察腦電信號(hào)的去趨勢(shì)互相關(guān)指數(shù)的變化,判斷觀察對(duì)象的注意力集中狀態(tài)。這對(duì)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(shì)互相關(guān)的注意力腦電信號(hào)分析。研究了另一種腦電信號(hào)分析算法,即多重分形去趨勢(shì)互相關(guān)分析算法。這種算法通過(guò)不同的參數(shù)和角度證明腦電信號(hào)的多重分形特性。通過(guò)分析得到以下結(jié)論:注意力集中時(shí),腦電信號(hào)的多重分形去趨勢(shì)互相關(guān)指數(shù)更接近常數(shù)1,所以腦電信號(hào)的長(zhǎng)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(guò)Android移動(dòng)智能設(shè)備,腦電信號(hào)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(hào),存儲(chǔ)腦電信號(hào)。本系統(tǒng)對(duì)日后更深入研究腦電信號(hào)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R318;TN911.6

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