• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.683-691
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• 2019

In this paper, we propose a novel method to quantify the neural synchronization between subjects in the collaborative process through electroencephalogram (EEG) hyperscanning. We hypothesized that the neural synchronization in EEGs will increase when the communication of the operators is smooth and the teamwork is better. We quantified the EEG signal for multiple subjects using a representative EEG quantification method, and studied the changes in brain activity occurring during collaboration. The proposed method quantifies neural synchronization between subjects through bispectral analysis. We found that phase synchronization between EEGs of multi subjects increased significantly during the periods of collaborative work. Traditional methods for a human error analysis used a retrospective analysis, and most of them were analyzed for an unspecified majority. However, the proposed method is able to perform the real-time monitoring of human error and can directly analyze and evaluate specific groups.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.587-594
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• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.26-26
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• 2004

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.5
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• pp.15-18
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• 2013

This paper presents an analog front-end for electroencephalogram(EEG) signal processing. Since EEG signals are typically weak and located at very low frequencies, it is imperative to implement an amplifier with high gain, high common-mode rejection ratio(CMRR) and good noise immunity at very low frequencies. The analog front-end of this paper consists of a programmable-gain instrumentation amplifier and a band-pass filter. A frequency chopping technique is employed to remove the low-frequency noise. The circuits were fabricated in 0.18um CMOS technology and measurements showed that the analog front-end has the maximum gain of 60dB and >100dB CMRR over the programmable gain range.

• Journal of the Korean Society of Physical Medicine
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• v.5 no.3
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• pp.313-322
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• 2010

Purpose : This study was to evaluate the effects of cognitive rehabilitation training on the cognitive decline of dementia patients. Therefore the purpose of this study was to examine the influence of brain activation according to bio-feedback training in dementia. Methods : Ten dementia patients were recruited this study. Experiment was performed for 30min per session, five times a week through 4 week and two measurements before and after bio-feedback training. Brain activity was measured by Korea Electroencephalogram(EEG) system. Statistical analysis was used Wilcoxon signed rank test to know difference of EEG between pre and post-test in each group and Mann-Whitney U test was to know difference between experimental and control group. Results : Significant improvement of slow-alpha wave was observed following bio-feedback in experiment group. There was no significant change in experiment and control group. Conclusion : In this study, the bio-feedback training was effective in improving slow-alpha wave in dementia patients. It is suggested that bio-feedback training with dementia patients can be useful to ameliorate the cognitive decline. And it will be effective for prevention of cognitive function decline.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.230-234
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• 2000

Thermal comfort by wearing clothes is the important element which gives influence to a clothing comfort. The thermal comfort of clothes have been evaluated by sensory test and physical property of clothes material. To evaluate a thermophysiological comfort. a new evaluation method which measures the physiological response such as electroencephalogram(EEG) is attracting the attention of many people. In the chilly environment, the EEGs in t재 kinds of thermal conditions : with and without clothes were measured. By utilizing the chaos analysis, the behavior of the obtained EEGs were quantiatively expressed in the correlation dimension. As a result, the correlation dimension of the EEGs in being thermal comfortable feeling by putting on clothes, was bigger than the correlation dimension of the EEGs in being cold and discomfort. These results suggest that chaotic analysis of EEG is effective to the quantitative evaluation of thermal esthesis.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.286-294
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• 2008

This work investigates the characteristics of brain interactions of experienced Zen-Buddhist practitioners by obtaining multichannel EEG (electroencephalogram) data. Brain interactions were compared among three phases-40-minute meditation (M), 5-minute Chakra-focusing practice (Z) and rest with closed eyes (R). The similarity index S, developed in nonlinear dynamical system theory, was employed to measure the degree of possibly asymmetric coupling. Meditators exhibited, overall, stronger interactions among multiple cortical areas in meditation stages M and Z than in the R state. This enhancement was greater in the M stage when the meditator was accompanied by a thought-free and fully consciousness state. In the high-frequency band (>13Hz), the interdependence was also higher in both meditation stages than at baseline rest. However, the interaction strength, especially in the posterior regions, was greatest in the Z stage, which involved internal attention. Few electrode pairs were observed with significant pair-wise asymmetry in the Z state. The similarity is a possible characteristic of dense reciprocal and strong mutual interactions between multiple cortical areas during meditation - especially in the Z state in the high-frequency band. These results demonstrate that profound Zen meditation induces various dynamic states in different phases of meditation, possibly reflected by nonlinear interdependence measure.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.240-247
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• 2006

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.366-372
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• 2002

EEG is the electrical signal, which is occurred during information processing in the brain. These EEG signal are measured by non-invasive method. EEG has many useful information for brain activity, but artifacts which are included in EEG prevents EEG analysis, so many efforts are devoted to remove these artifacts in EEG. However, this study is going to analysis the feature of the EEG mixed with artifacts in forward-looking way, by using this way, we have found the possibility that is actually applicable to system such as control system. We have made feature difference after the linear as well as nonlinear analysis regarding EEG including typical artifacts, eye-blinking, eye rolling, muscle, and so forth.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.