• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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• pp.68-83
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• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.521-528
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• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• Journal of KIISE
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• v.44 no.9
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• pp.887-892
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• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Korean Journal of Acupuncture
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• v.28 no.3
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• pp.85-98
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• 2011

Objectives : The aim of this thesis is to examine the effect of acupuncture treatment at the GB37 on normal humans by using the power spectral analysis of the EEG. Methods : EEG (Electroencephalogram) power spectrum exhibits site-specific and state-related differences in specific frequency bands. In this thesis, the power spectrum was measured by the complexity. the 32 channels EEG study was carried out in the 13 subjects (12 males ; age=22.58 years old, 1 females ; 22 years old). Results : In the ${\alpha}$ (alpha) band, the power values at F7, F3, F4, F8, FTC2, C4, T4, CP1, CP2, TCP2, TT2, Pz, P4, Po1, Po2, O1, Oz, O2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the ${\beta}$ (beta) band, the power values at Cz, C4, T4, Tcp1, T6, Po1, O1, Oz, O2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the ${\delta}$(delta) band, the power values at Fp1, TT2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the $\theta$ (theta) band, the power values at Fp1, F8, FTC2, Pz channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. Conclusions : This results suggest that the acupuncture treatment at the GB37 significantly mostly change the power spectrum value on the alpha (18 channels), beta (9 channels) bands.

• Journal of Veterinary Clinics
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• v.17 no.2
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• pp.359-367
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• 2000

The aim of this study was to evaluate the effects of propofol on cortical electroencephalogram (EEG) in seven dogs. Propofol infusion was accomplished from low concentration to high concentration in series, and each concentration was infused for 20 minutes (M0: 0, M0.5: 0.5, M1.0:1.0, and M1.5: 1.5 mg/kg/min of infusion rate). EEG was recorded via needle electrode placed at Cz, which was applied to International 10-20 system. Arterial blood pressure. blood gas analysis and ECG were also measured. Hoemodynamics, Pa$CO_2$, PaO$_2$, heart rate and respiratory rate were variable, but were net significant(p>0.05). The power spectra of EEG in every concentration was compared wish those of control (MO). The powers at a1l frequencies at M1.0 and Ml.5 were decreased. Especially, the powers of the frequencies over 20 Hz were significantly decreased (p<0.O5). Powers at frequencies between 8 and 15Hz at MO.S were significantly increased (p<0.05) in response to the painful stimuli. It was inferred that they may reflect activity of the brain which is consciously processing the external Stimuli. Like the Power spectra, al1 the band powers of He EEG ($\delta$ 1-4, $\theta$4-8, $\alpha$ 8-13, $\beta$L13-21. $\beta$H 21-30, \ulcorner 30-50, and total 1-5OHz) were decreased in proportion to the increase of infusion rate at M1 .0 and M1.5. Especially, decrease of $\beta$H and ${\gamma}$ were significant(p<0.01). At M0.5, $\alpha$ band was significantly increased(p<0.05) among all the bands. Seizure activities which were concide with occurrence of spike wave were shown in all dogs at Ml .0 and M1.5.

• Journal of People, Plants, and Environment
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• v.21 no.6
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• pp.575-589
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• 2018

This study analyzed the changes in Electroencephalogram(EEG) and Electrocardiogram(ECG) depending on the healing environment in order to find a way to improve the forest healing program based on the healing environment in response to the demand for qualitative improvement of the program since the program is a charged service. This study selected eight sites running forest healing programs at four national healing forests (i.e., Saneum, Cheongtaesan, Daegwanryeng, and Jangseong) - two routes per national healing forest - considering forest environments. This study chose NUMBER standard sampling plots ($20{\times}20m$) and measured three atmospheric environment items, seven physical environment items, two soil environment items, and eight vegetation environment items including forest sound and anion at each plot to evaluate physiological changes in it. EEG and ECG, which have been widely used in forest healing evaluation, were utilized as criteria. Seventy three subjects were selected with taking the age, drug, caffeine, smoking, and the time of last meal into consideration. As a result, EEG changes were correlated with three atmospheric environment items, six physical environment items, one soil environment item, and two vegetation environment items. ECG changes were significantly correlated with two atmospheric environment items, six physical environment items, two soil environment items, and two vegetation environment items (p<.05). It is expected that 11 environmental factors such as temperature, density, and altitude affecting EEG (e.g., alpha balance and gamma balance) and ECG (e.g., HRV mean) could be used as effective tools in developing more differentiated programs for improving healing effects.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.237-242
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• 2020

Construction noise is among the most critical stressors that adversely affect the quality of life of the people residing near construction sites. Many countries strictly regulate construction noise based on sound pressure levels, as well as timeslots and type of construction equipment. However, individuals react differently to noise, and their tolerance to noise levels varies, which should be considered when regulating construction noise. Although studies have attempted to analyze individuals' stress responses to construction noise, the lack of quantitative methods to measure stress has limited our understanding of individuals' stress responses to noise. Therefore, the authors proposed a quantitative stress measurement framework with a wearable electroencephalogram (EEG) sensor to decipher human brain wave patterns caused by diverse construction stressors (e.g., worksite hazards). This present study extends this framework to investigate the feasibility of using the wearable EEG sensor to measure individuals' emotional stress responses to construction noise in a laboratory setting. EEG data were collected from three subjects exposed to different construction noises (e.g., tonal vs. impulsive noises, different sound pressure levels) recorded at real construction sites. Simultaneously, the subjects' perceived stress levels against these noises were measured. The results indicate that the wearable EEG sensor can help understand diverse individuals' stress responses to nearby construction noises. This research provides a more quantitative means for measuring the impact of the noise generated at a construction site on neighboring communities, which can help frame more reasonable construction noise regulations that consider various types of residents in urban areas.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Anxiety and mood
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• v.20 no.1
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• pp.8-16
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• 2024

Objective : The purpose of this study is to compare the signal obtained from the frontal 2-electrodes EEG with that obtained from the temporal, central, and parietal 2 electrodes. Methods : EEGs were recorded in a total of 67 patients with major depressive disorder (MDD), 104 patients with schizophrenia (SCZ), and 29 patients with Alzheimer's disease (AD). For each disease group, there were healthy controls (HC) that were paired accordingly (HC1=69, HC2=104, HC3=27). The following measurements were compared across electrodes: band power, alpha peak frequency (APF), APF power, alpha asymmetry (AA), and Kolmogorov complexity (KC). Results : Statistically significant differences were found in band power measured from frontal electrodes compared to electrodes placed in other locations. Specifically, the power of theta waves was measured higher in the temporal electorodes, alpha 1 and alpha 2 waves in the parietal, beta 1 and beta 2 in the central, and gamma waves in the temporal electrodes. Both SCZ and AD patients showed increased theta power in all electrodes. In SCZ patients, APF decreased in the central and temporal electrodes, but the APF power analysis showed no difference between the patients and controls. Additionally, AD patients exhibited increased AA in the central EEG, while SCZ patients showed decreased KC in the parietal and temporal electrodes. Conclusion : Depending on the electrode location, sensitive EEG frequencies differed. Compared with signals from other electrodes, frontal EEG in MDD patients revealed generally constant signal values, though the temporo-parieto-central electrodes appeared to be more reliable in SCZ and AD patients.