脑电信号的小波相关与互信息分析
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摘要
研究认知过程与脑电活动具有重要的实际科学意义，受到越来越多研究人员的注意，已成为当今研究的一个热点。神经系统的基本功能是信息的传递、处理和存储，所有高级神经功能都是以各级神经元细胞内及细胞外的生化和电生理活动为基础。大脑内部的信息传递是以生物电的形式存在。无论是头皮脑电还是皮层微电极采集的脑电信号都是大量细胞级电活动的总和，脑电信号包含了丰富的神经生理信息。认知刺激将直接改变神经细胞的电生理活动基础，这种变化会反映到脑电信号上。因此通过对脑电信号的分析，可以提取出反映不同认知条件下的特征变化情况。对于研究不同认知条件下的大脑内部生理的变化有重要意义。
本文从相关分析开始，针对脑电信号的非线性特性，提出了一种良好的脑电的时变相关分析。这种方法对脑电分析的时频分辨率问题得到了很好的解决。我们采用了基于连续小波变换的相关分析方法来分析不同脑区之间的脑电数据的相互关系。时变小波幅度谱提供了不同时段相关的程度的定量分析，而相位则揭示了不同脑区之间信息交换的方向问题。此外，本文还研究了儿童进行中英文句法判别过程中的不同脑区之间脑电数据的相关情况。脑电数据是基于一个中英文句子的辨识的认知过程而采集。从我们的研究结果可以初步看出在中文和英文句子辨识过程中大脑通信模式有明显的区别。
另外，我们还尝试采用脑电信号的信息量变化以及大脑皮层不同部位间的信息传递来反映认知过程中的脑的功能和生理状态变化。为了描述大脑皮层及大脑内部相关性，我们研究了基于自适应单元分区的互信息分析方法，并对句子识别认知任务下采集的脑电信号做了分析。结果表明在中文和英文句子识别中大脑不同区域之间的信息交换有明显的区别。最后，我们采用脑电彩色地形图的方式对我们的研究结果进行展示。
本论文研究结果表明：小波相关和互信息分析可能成为认知脑电的分析一个新的方向；大脑在进行中英文句法辨识的过程中不同脑区之间信息传递方式有明显的不同。这将对中国学生进行中英文双语教学的教育研究起到积极的作用。
Electroencephalogram (EEG) during a cognitive process has attracted more attention in the past years due to its great practical and scientific significance. The basic properties of the neuronal system are information processing, transferring, and memorization, which are based on the extra cellular and intra cellular biochemistry reactions and electrophysiological activity. EEG has been widely accepted as the summation effect of the electrical activities of billions neurons on the scalp, which is full of physiological information within the brain. Experiments have shown the imprinting of the cognitive difference in the EEG recordings. We picked up the character difference during the different cognitive process by analyzing the EEG
We introduced coherent analysis to the EEG analysis. In this paper, because of the non-linearity of the EEG, we propose a novel method for time-varying coherent analysis of EEG The proposed method provides an effective way for quantifying synchrony with both temporal and spectral resolution. By using the continuous wavelet transform, wavelet spectrum is analysis from the EEG data. The wavelet magnitude spectra provide the degree of coherence and the wavelet phase serves to indicate the direction of information flow between two channels on different cortical regions. Besides, we researched the EEG coherence between different regions during the child recognized the Chinese and English sentences. Real EEG data is collected based on a cognitive target. It is observed from our research result that there are obvious differences of the mode of the information exchanged between the identification in Chinese and English sentences.
Besides, we introduced information measure to evaluate the complexity evolution of EEG and the information transferring across the cerebral cortex during the cognitive process. To describe the interactions across the cerebral and inside the brain, we researched the mutual information (MI), which is estimated with adaptive portioning method and calculated the global mutual information during the sentences recognizing. The result also indicated that the mutual information has obvious differences between the identification in Chinese and English sentences. Finally, we use the EEG magnitude relief map to demonstrate the result.
The results of our study indicate the possibility of using wavelet coherence and mutual information to study the EEG during cognitive process. There has obvious difference of the information exchanges during the Chinese and English sentences recognizing. These are helpful for the research on the bilingual learning in both English and Chinese for Chinese students.
本文从相关分析开始，针对脑电信号的非线性特性，提出了一种良好的脑电的时变相关分析。这种方法对脑电分析的时频分辨率问题得到了很好的解决。我们采用了基于连续小波变换的相关分析方法来分析不同脑区之间的脑电数据的相互关系。时变小波幅度谱提供了不同时段相关的程度的定量分析，而相位则揭示了不同脑区之间信息交换的方向问题。此外，本文还研究了儿童进行中英文句法判别过程中的不同脑区之间脑电数据的相关情况。脑电数据是基于一个中英文句子的辨识的认知过程而采集。从我们的研究结果可以初步看出在中文和英文句子辨识过程中大脑通信模式有明显的区别。
另外，我们还尝试采用脑电信号的信息量变化以及大脑皮层不同部位间的信息传递来反映认知过程中的脑的功能和生理状态变化。为了描述大脑皮层及大脑内部相关性，我们研究了基于自适应单元分区的互信息分析方法，并对句子识别认知任务下采集的脑电信号做了分析。结果表明在中文和英文句子识别中大脑不同区域之间的信息交换有明显的区别。最后，我们采用脑电彩色地形图的方式对我们的研究结果进行展示。
本论文研究结果表明：小波相关和互信息分析可能成为认知脑电的分析一个新的方向；大脑在进行中英文句法辨识的过程中不同脑区之间信息传递方式有明显的不同。这将对中国学生进行中英文双语教学的教育研究起到积极的作用。
Electroencephalogram (EEG) during a cognitive process has attracted more attention in the past years due to its great practical and scientific significance. The basic properties of the neuronal system are information processing, transferring, and memorization, which are based on the extra cellular and intra cellular biochemistry reactions and electrophysiological activity. EEG has been widely accepted as the summation effect of the electrical activities of billions neurons on the scalp, which is full of physiological information within the brain. Experiments have shown the imprinting of the cognitive difference in the EEG recordings. We picked up the character difference during the different cognitive process by analyzing the EEG
We introduced coherent analysis to the EEG analysis. In this paper, because of the non-linearity of the EEG, we propose a novel method for time-varying coherent analysis of EEG The proposed method provides an effective way for quantifying synchrony with both temporal and spectral resolution. By using the continuous wavelet transform, wavelet spectrum is analysis from the EEG data. The wavelet magnitude spectra provide the degree of coherence and the wavelet phase serves to indicate the direction of information flow between two channels on different cortical regions. Besides, we researched the EEG coherence between different regions during the child recognized the Chinese and English sentences. Real EEG data is collected based on a cognitive target. It is observed from our research result that there are obvious differences of the mode of the information exchanged between the identification in Chinese and English sentences.
Besides, we introduced information measure to evaluate the complexity evolution of EEG and the information transferring across the cerebral cortex during the cognitive process. To describe the interactions across the cerebral and inside the brain, we researched the mutual information (MI), which is estimated with adaptive portioning method and calculated the global mutual information during the sentences recognizing. The result also indicated that the mutual information has obvious differences between the identification in Chinese and English sentences. Finally, we use the EEG magnitude relief map to demonstrate the result.
The results of our study indicate the possibility of using wavelet coherence and mutual information to study the EEG during cognitive process. There has obvious difference of the information exchanges during the Chinese and English sentences recognizing. These are helpful for the research on the bilingual learning in both English and Chinese for Chinese students.
引文
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[2] 刘建平，郑崇勋等 脑电信号的分析----一种探索大脑功能状态及活动规律的途径，国外医学生物医学工程分册 5，vol 18，1995
[3] 李勇 脑电信号现代分析方法的发展 国外医学生物医学工程分册 4v19，1996
[4] C.Andrew, G.. pfurscheller; Dependence of coherence measurements on EEG derivation type, Med.&Biol.Eng.& comput,1996,34,232-238
[5] Sabine Weiss, Peter Rappelsberger, EEG coherence within 13-18 Hz band as a correlate of a distinct lexical organisation of concrete and abstract nouns in humans, Neuroscience Letters, 209(1996) 17-20
[6] R.W.Thatical associations and EEG coherence: A two-compartmental model,ibid, 64, pp58-75
[7] Barbel Schack, Peter Rappelsberger; Adaptive phase estimation and its application in EEG analysis of word processing Journal of Neuroscience Methods 93(1999)49-59
[8] B. Schack, G. Grieszbach; Dynamic cross-spectral analysis of biological signals by means of biovariate ARMA processes with time-dependent coefficients. Med.& Bio. Eng. &Com. 1995, 33, 605-610
[9] B. Schack, W. Krause; Instantaneous Coherence as a Sensible Parameter for Considering Human Information Processing 1996 IEEE Proceedings of ICPR' 96; 45-49
[10] Rudolf Rabenstein, Thorsten Barosch; Wavelet Analysis of Meteorological Date. EuroSun'96 1545-1549
[11] Brockwell, P. J. and Davis, R.A(1991) Time series: theory and methods;(Springer-Verlag, New York)
[12] Gunther, R(1983) On the convergence of some adaptive estimation procedures; Math Operationsforsch. u. Statist. ser. statist,14, p535-550
[13] Gunther, R(1988) Adaptive estimation and preduction for an extension of the autoregressive multiple time series model Stst., 19, p557-564
[14] Schack B. Adaptive Verfahren zur Spektralanlyse instationarer mehrdimensionaler biologischer Signale, Thesisi, Technical University 2menau, 1997
[15] Schack B. Dynamic topographic spectral analysis of cognitive rocessesIn: UhlCh, editor. Model-based Analysis of Neurophysiological Brain Functioning. Berlin: Springer, 1999: 230-951
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