腦電信號(hào)分析在認(rèn)知功能中的研究.pdf

1、重慶大學(xué)碩士學(xué)位論文腦電信號(hào)分析在認(rèn)知功能中的研究姓名：張新申請(qǐng)學(xué)位級(jí)別：碩士專業(yè)：生物醫(yī)學(xué)工程指導(dǎo)教師：@2010-04重慶大學(xué)碩士學(xué)位論文 英文摘要 II ABSTRACT Electroencephalogram (EEG) contains a large number of physiological and pathological information. It can be measured directly

2、 on human’ s body and is suitable for clinical application, can provide diagnostic evidence for some brain diseases, even can be used to treat certain brain diseases. Recently, research on cog

3、nitive function has got more and more attention. Effective analysis and evaluation to cognitive function have great significance on detection and treatment of cognitive disorder disease. The paper

4、 in the basis of EEG amplifier hardware, using Visual C++ 6.0 to compile the corresponding EEG acquisition software, constitute the signal acquisition module of the detection of cognitive EEG

5、testing and analysis platform, display the EEG with the waveform in real time, and store the data with a separate document to facilitate subsequent analysis and processing. And for cognitive fu

6、nction, design cognitive EEG test to reflect the different states of brain function and collect the EEG under the state of quiet and eyes closed, quiet and eyes opened, calculation test, direct

7、ive force test and visual spatial tests. Based on the study of classical EEG analysis, use software filtering technology to analyze and process the EEG，Provides a zero- phase digital filter、Inde

8、pendent Component Analysis 、 Wavelet Transform and other EEG analysis method to pre- treatment the collected EEG to remove noise and interference, and gets the signal power spectral to give a reference

9、cognitive analysis of EEG. Coherence detection methods is used to extract the 40Hz EEG signal reflecting the wake level of concentration of paroxysmal in the process of advanced information processing, it

10、 can provide the basis of cognitive status for the selected EEG data segment. Strong nonlinearity is a prominent characteristic of the nervous system. EEG is a chaotic signal with wide range o

11、f nonlinear dynamic characteristics, and n onlinear analysis of EEG can provide the changing track of the brain cognitive activities. In this paper, EEG was analyzed by nonlinear dynamics method

12、, and the fractional dimension and approximate entropy of EEG was quickly and efficiently extracted to describe the brain cognitive functions and explore their relationship with cognitive functio

13、n, and then used as an evaluation index of cognitive function. In the paper, fractal dimensions and approximate entropies of EEG data in different states was calculated. The results indicates t