• International conference on construction engineering and project management
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• 2015.10a
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• pp.179-182
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• 2015

Detecting and assessing human-related risks is critical to improve the on-site safety condition and reduce the loss in lives, time and budget for construction industry. Recent research in neural science and psychology suggest inattentional blindness that caused by overload in working memory is the major cause of unexpected human related accidents. Due to the limitation of human mental workload, laborers are vulnerable to unexpected hazards while focusing on complicated and dangerous construction tasks. Therefore, detecting the risk perception abilities of workers could help to identify vulnerable individuals and reduce unexpected injuries. However, there are no available measurement approaches or devices capable of monitoring construction workers' mental conditions. The research proposed in this paper aims to develop such a measurement framework to evaluate hazards through monitoring electroencephalogram of labors. The research team developed a wearable safety monitoring helmet, which can collect the brain waves of users for analysis. A bispectrum approach has been developed in this paper to enrich the data source and improve accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.4
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• pp.195-202
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• 2016

There have been many studies on subject tests for 3D contents using 3D glasses, but there is a limited research for 3D contents using autostereoscopic display. In this study, we investigated to assess usability of electroencephalogram (EEG) as an objective evaluation for 3D contents with different quality using autosteroscopic display, especially for lenticular lens type. The image with optimal quality and the image with distorted quality were separately generated for autostereosopic display with lenticular lens type and displayed sequentially through lenticular lens for 26 subjects. EEG signals of 8 channels from 26 subjects exposed to those images were detected and correlation between EEG signal and the quality of 3D images were statistically evaluated to check differences between optimal and distorted 3D contents. What we found was that there was no statistical significance for a wave vibration, however b wave vibration shows statistically significant between optimal and distorted 3D contents. b wave vibration observed for the distorted 3D image was stronger than that for the optimal 3D image. This results suggest that subjects viewing the distorted 3D contents through lenticular lens experience more discomfort or fatigue than those for the optimum 3D contents, which resulting in the greater b wave activity for those watching the distorted 3D contents. In conclusion, these results confirm that electroencephalogram (EEG) analysis can be used as a tool for objective evaluation of 3D contents using autosteroscopic display with lenticular lens type.

• Sleep Medicine and Psychophysiology
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• v.15 no.1
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• pp.5-11
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• 2008

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA ; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA ; 3) electrophysiological properties of narcolepsy could be characterized by the DFA ; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.786-791
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• 2011

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1914-1922
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• 2008

In this paper, examined characteristics of EEG(electroencephalogram) variation when the stimulation was given to lung-wan(CV12), Shin-gwol(CV8) and Gwan-won(CV4) which were some of the acupuncture point, through indirect moxibustion and No stimulation. The EEG signals were measured before the stimulation, during the stimulation, and 1 hour after the stimulation. The measured time domain data were converted to the frequency domain data FFT(Fast Fourier Transform) and frequency power spectrum. Then the $\alpha,\beta,\delta$, and $\theta$ waves were analyzed for variation to the amplitude of vibration according to the stages of stimulation.

• International Journal of Contents
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• v.9 no.4
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• pp.1-10
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• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.

• Dementia and Neurocognitive Disorders
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• v.21 no.3
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• pp.93-102
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• 2022

Background and Purpose: The effects of high-intensity interval training (HIIT) interventions on functional brain changes in older adults remain unclear. This preliminary study aimed to explore the effect of physical exercise intervention (PEI), including HIIT, on cognitive function, physical performance, and electroencephalogram patterns in Korean elderly people. Methods: We enrolled six non-dementia participants aged >65 years from a community health center. PEI was conducted at the community health center for 4 weeks, three times/week, and 50 min/day. PEI, including HIIT, involved aerobic exercise, resistance training (muscle strength), flexibility, and balance. Wilcoxon signed rank test was used for data analysis. Results: After the PEI, there was improvement in the 30-second sit-to-stand test result (16.2±7.0 times vs. 24.8±5.5 times, p=0.027), 2-minute stationary march result (98.3±27.2 times vs. 143.7±36.9 times, p=0.027), T-wall response time (104.2±55.8 seconds vs.71.0±19.4 seconds, p=0.028), memory score (89.6±21.6 vs. 111.0±19.1, p=0.028), executive function score (33.3±5.3 vs. 37.0±5.1, p=0.046), and total Literacy Independent Cognitive Assessment score (214.6±30.6 vs. 241.6±22.8, p=0.028). Electroencephalography demonstrated that the beta power in the frontal region was increased, while the theta power in the temporal region was decreased (all p<0.05). Conclusions: Our HIIT PEI program effectively improved cognitive function, physical fitness, and electroencephalographic markers in elderly individuals; thus, it could be beneficial for improving functional brain activity in this population.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.3
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• pp.25-36
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• 2024

Purpose : After coronavirus disease 2019 pandemic, many people around the world became aware of respiratory diseases and developed increased interest in health and exercise. As the elderly population grows larger, the prevalence of brain diseases and respiratory conditions increases. This study aimed to investigate the impact of dynamic neuromuscular stabilization (DNS) exercises on pulmonary function and electroencephalogram (EEG) findings in the elderly population. Methods : In this study, we measured the pulmonary function and performed EEG of 30 elderly individuals. The participants were randomly divided into an experimental group (n=15) and a control group (n=15). The experimental group completed a DNS exercise training protocol, while the control group performed simple exercise training. After the exercise, the pulmonary function and EEG testing were repeated, and the amounts of change within and between groups were determined. Results : In terms of pulmonary function, the experimental group's forced vital capacity (FVC) and forced expiratory volume at 1 second (FEV1) increased significantly after the exercise (p<.05), while the FVC differed significantly between groups (p<.05). In terms of EEG findings, the relative alpha waves significantly decreased in the F3 region (p<.05), while the relative beta waves significantly increased in the F4 region (p<.05). The relative gamma waves increased significantly in the Fp2 and P3 regions (p<.05), while the values in the F3 region differed significantly between groups (p<.05). Conclusion : DNS exercises performed by this elderly population demonstrated significant clinical value by providing fundamental data about both pulmonary function and EEG activity.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.17-22
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• 2020

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.96-101
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• 2005

Status epilepticus is a medical emergency, so that rapid and vigorous treatment is required to prevent neuronal damage and systemic complication. Status epilepticus is generally defined as a continuous or intermittent seizure or an unconscious condition after the onset of seizure, lasting for 30 minutes or more. We report here the outcome of status epilepticus. We retrospectively reviewed medical record of 15 patients who were diagnosed with status epilepticus at the Asan Medical Center from January 2003 to February 2004. This outcome was evaluated considering various factors such as age of patients, history of seizures, neurologic impairment, etiology, mortality, return to baseline and initial electroencephalogram (EEG) findings. The range of age was between 1 to 79 years old and the longest duration of treatment was 118 days. Most patients were treated by using pentobarbital, midazolam, phenobarbital and other antiepileptic drugs. The overall mortality was 5 (33%) out of 15 patients. The mortality was related to etiology, underlying other medical conditions and initial EEG findings. 5 (55%) out of the 9 patients with acute etiology, 5 (71%) out of the 7 patients with a multifocal or burst-suppression EEG activity, and 3 (60%) out of the 5 patients with other medical disease were related to mortality. This data demonstrate high mortality due to status epilepticus. Mortality is related to etiology, other medical conditions and abnormalities on the initial EEG.