• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.1
• /
• pp.7-12
• /
• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.68-83
• /
• 2007

Background and Purpose: According to chaos theory, irregular signals of electroencephalogram can interpretated by nonlinear method. Chaotic nonlinear dynamics in EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze EEG by correlation dimension and do Correlation Analysis of correlation dimension and K-POMS factors score. Method: EEG raw data were measured during 15 minutes and choosed 40 seconds. We calculated correlation dimension and used surrogate data method for checking nonlinear data. After then do correlation analysis. Result and Conclusion: Correlation dimension of channel 6, channel 7 and channel 8 are showed significant correlation with vigor factor.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.116-127
• /
• 2007

Background and Purpose: Acording to chaos theory, irregular signals of electroencephalogram can interpretated by nonlinear method. Chaotic nonlinear dynamics in EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze EEG by correlation dimension and do Correlation Analysis of correlation dimension and cold-heat score Method: EEG raw data were measured during 15 minutes and choosed 40 seconds. We calculated correlation dimension and used surrogate data method for checking nonlinear data. After then do correlation analysis Result and Conclusion: Correlation dimension of channel 7 and channel 8 are showed significant correlation with cold score.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.128-140
• /
• 2007

Purpose: We performed this study to examine the correlationship between EEG complexity and impedance cardiography data using correlation analysis. Method: This study performed on 30 healthy subjects(16 males, 14 females). Before and after natural respiration, ICG data were recorded, and EEG raw data were measured by moving windows during 15 minutes. The correlation dimension(D2) was calculated from 15 minutes data. 8 channels EEG data were analysed with 9 index of ICG data by correlation analysis. Result: 1. ACI of impedance cardiography had significant correlationship with ch.4 of EEG complexity(p=0.03). 2. VI of impedance cardiography had significant correlationship with ch.3 of EEG complexity(p=0.034) and ch.4 of EEG complexity(p=0.017). 3. HR, TFC, PEP, LVET, STR of impedance cardiography had no significant correlationship with all of 8 channel EEG complexity. Conclusions: These results suggest that nonlinear analysis of EEG and impedance cardiography have some significant correlationship. And it can make out relationship between brain system and cardiovascular system. In the future, therefore, more study of these fields are necessary.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.36
• /
• pp.61-69
• /
• 1995

The diagnosis has an very important role in curing dementia. But there was not the effective method to diagnose it until now. In this paper we analyzed the EEG in Alzheimer's disease and normal control groups to differentiated them by nonlinear parameter such as the correlation dimension. And we propose the nonlinear analysis of EEG in Alzheimer's disease as a useful tool of early diagnosis of it.

• Annals of Clinical Neurophysiology
• /
• v.4 no.1
• /
• pp.28-33
• /
• 2002

Backgrounds and objective : EEG reflect dynamic changes of continuous neuronal activities by internal and external stimuli. The aim of this study is to quantify nonlinearly the local dynamic differences among EEG data corresponding to different states of brain. Methods : EEG was recorded from twelve healthy normal subjects(mean age, 29.7 years; 8 men and 4 women) using digital EEG machine. 18-channel EEG data were selected during eyes closed(EC), eyes open(EO), and mental arithmetic(MA) in each subject. Correlation dimension(D2) and largest Lyapunov exponent(LLE) were calculated from three states and average value was mapped 2 dimensionally and compared with each other. Results : The distribution of D2 was relatively symmetric and its value was higher in frontal than in parieto-occipital region during EC. These findings were reversed during EO. Bilateral centro-temporo-parietal region showed high D2 value in MA compared with those in EC, which was more prominent in left side. LLE was larger than zero in all state and showed significant differences among EC, EO and MA(p=0.000). Conclusion : These results suggest that nonlinear analysis of EEG can quantify dynamic state of brain.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.96-115
• /
• 2007

Background and Purpose: Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different origins. Recently, because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to chaos theory, irregular signals of EEG can also result from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze correlation between the correlation dimension of EEG and psychological Test (TCI). Methods: Before and after moxibustion treatment, EEG raw data were measured by moving windows during 15 minutes. The correlation dimension(D2) was calculated from stabilized 40 seconds in 15 minutes data. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results: Correlation analysis of TCI test is calculated with deterministic non-linear data and stochastic non-linear data. 1. Novelty seeking in temperament is positive correlated with D2 of EEG on Fp. 2. reward dependence in temperament is positive correlated with D2 of EEG on T3,T4 and negative correlated with D2 of EEG on P3,P4. 3. self directedness in character is positive correlated with D2 of EEG on F4, P3. 4. Harm avoidance is negative correlated with D2 of EEG on Fp2, T3, P3. Conclusion: These results suggest that nonlinear analysis of EEG can quantify dynamic state of brain abolut psychological Test (TCI).

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.84-95
• /
• 2007

Background and Purpose: We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. According to chaos theory, irregularity of EEG signals can result from low dimensional deterministic chaos. A principal parameter to quantify the degree of Chaotic nonlinear dynamics is correlation dimension. The aim of this study was to analyze correlation between the correlation dimension of EEG and HRV(heart rate variability). We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. Methods: EEG raw data were measured by moving windows during 15 minutes. Then, the correlation dimension(D2) was calculated by each 40-seconds-segment in 15 minutes data, totally 36 segments. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results and Conclusion: Correlation analysis of HRV was calculated with deterministic non-linear data and stochastic non-linear data. 1. Ch1(Fp1), Ch4(F3), Ch4(F4) is positive correlated with In LF. 2. Ch1(Fp1), Ch3(F3) is positive correlated with In TF.

• Annals of Clinical Neurophysiology
• /
• v.4 no.1
• /
• pp.21-27
• /
• 2002

Background : Spatial analysis of EEG is a phenomenal assessment and not so informative for phase space and dynamic aspect of EEG data. In contrast, nonlinear EEG analysis attempts to characterize the dynamics of neural networks in the brain. We have analyzed the features of EEG nonlinearly in subjects with white matter change on brain MRI and compared the results with cognitive function in each. Methods : Digital EEG data were taken for 30 seconds in 9 subjects with white matter degeneration and in 5 healthy normal controls without white matter change on MRI. Then we analyzed them nonlinearly to calculate the correlation dimension(D2) using the MATLAB software. The cognitive function was assessed by 3MS(modified mini-mental state examination). The severity of white matter change was assessed by Scheltens scale. Results : The mean D2 value of normal control was greater than that of white matter degeneration group. The D2s of some channels were correlative with 3MS and degree of white matter degeneration significantly. Conclusions : nonlinear analysis of EEG can be used as one of adjuvant functional studies for prediction of cognitive impairment in subjects with white matter degeneration and subcortical white matter change can be influential on cognitive function and correlation dimension of EEG.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.442-450
• /
• 2002

In measuring EEG, which is widely used for studying brain function, EEG is frequently mixed with noise and artifact. In this study, the signals relevant to the artifact were distracted by applying ICA to EEG signal. First, each independent component which was assumed to be the source was separated by applying ICA to EEG which involved artifact relevant to the eye movement of a normal person. Next, the signal which was assumed to be artifact was removed from the separated 18 independent components, and the nonlinear analysis method such as correlation dimension and the Iyapunov exponent was applied to each reconstructed EEG signal and the original signal including artifact in order to find meaningful difference between the two signals and infer the anatomical localization of its source and distribution. This study shows it is possible not only to analyze the brain function visually and spatially for visually complex EEG signal, but also to observe its meaningful change through the quantitative analysis of EEG by means of the nonlinear analysis.