The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collect...The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.展开更多
EEG inverse problem has great significance and importance for both cli nical and research applications. It discusses EEG dipole source localization pro blems solved by nonlinear local optimization methods, such as Lev...EEG inverse problem has great significance and importance for both cli nical and research applications. It discusses EEG dipole source localization pro blems solved by nonlinear local optimization methods, such as Levenberg-Marquar t b. This paper presents the relation between location errors and noise level on c ondition that the source number is known; if the source number is not known, the selected number in model may not equal to the actual one, and a computation is carried out and a corresponding discrimination criteria is proposed. Computer si mulation demonstrates that Levenberg-Marquardt algorithm is better than global methods if the source number is small.展开更多
基金International Joint Research Program from the Ministry of Science and Technology of Chinagrant number:20070667+1 种基金Education Commission of Chongqing of Chinagrant number:KJ081209
文摘The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.
文摘EEG inverse problem has great significance and importance for both cli nical and research applications. It discusses EEG dipole source localization pro blems solved by nonlinear local optimization methods, such as Levenberg-Marquar t b. This paper presents the relation between location errors and noise level on c ondition that the source number is known; if the source number is not known, the selected number in model may not equal to the actual one, and a computation is carried out and a corresponding discrimination criteria is proposed. Computer si mulation demonstrates that Levenberg-Marquardt algorithm is better than global methods if the source number is small.
