注意力脑电信号分析与脑机接口系统实现

发布时间：2018-05-26 08:35

本文选题：注意力脑电 + 去趋势互相关分析　；参考：《南京邮电大学》2017年硕士论文

【摘要】：随着社会的发展,脑力劳动占据人类活动的比重逐渐增高,注意力是否集中直接影响到了工作效率。因此,通过分析不同注意任务中的脑电信号,并进行分类具有重要意义。本文正是基于这一目标,通过对不同注意任务(冥想状态和放松状态)中的脑电信号进行低通滤波。然后提取包含低频波段的脑电信号。最后依次使用去趋势互相关算法(Detrended Cross-Correlation Analysis,简称DCCA)和多重分形去趋势互相关分析算法(Multifractal Detrended Cross-Correlation Analysis,简称MF-DCCA)分析不同注意任务中的低频波段脑电信号。以达到准确判断大脑注意力活动状态的目的。本文主要研究内容有以下三点:一、基于去趋势互相关的注意力脑电信号分析。使用去趋势互相关算法分别对受试者冥想状态和放松状态时的脑电信号进行分析计算,得到各自去趋势互相关指数。通过分析结果,我们发现在不同注意力状态,脑电信号的去趋势互相关指数有明显区别。注意力集中时,脑电信号的去趋势互相关指数更接近常数1,因此,注意力集中,脑电信号的长程相关性更强。因此可以通过观察脑电信号的去趋势互相关指数的变化,判断观察对象的注意力集中状态。这对辅助脑疾病康复治疗具有重要意义。二、基于多重分形去趋势互相关的注意力脑电信号分析。研究了另一种脑电信号分析算法,即多重分形去趋势互相关分析算法。这种算法通过不同的参数和角度证明脑电信号的多重分形特性。通过分析得到以下结论:注意力集中时,脑电信号的多重分形去趋势互相关指数更接近常数1,所以脑电信号的长程相关性更强。三、基于Android与Java EE脑机接口系统实现。为了让研究结果具有实际意义,本文通过Android移动智能设备,脑电信号传感器以及服务器端的Web应用搭建了一套脑机接口。本系统可以实时采集、分析脑电信号,存储脑电信号。本系统对日后更深入研究脑电信号具有重要意义。
[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.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R318;TN911.6

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