• Journal of the Korea Institute of Building Construction
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• v.20 no.1
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• pp.93-102
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• 2020

The purpose of this study is to find out how to measure the stress related to accidents at the construction site promptly and conveniently to prevent safety accidents of construction workers. Accordingly, we analyzed the correlations between the questionnaire tool index that measures the stress associated with complex psychology of humans by integrating emotion, cognition, physical and behavioral responses, and basic brain waves, SEF-90, concentration, stress index from brain wave. As a result, which had the highest correlation with the stress measured through the questionnaire, was the SEF-90, and the regression analysis between two independent variables yielded a specific regression equation. This suggests the possibility of measuring the integrated stress of construction workers through the EEG signal at the construction site, and it can be used for the safety management of the construction site in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.62-68
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• 2012

Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user's inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.75-79
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• 1997

The present study was designed to investigate whether there is a consistint changes in the signals from the central and autonomic nervous systim due to olfactory stimulation. The olfactory stimuli were 0.6% orange and 2.5% valeric acid and the stimuli through the olfactory stimulator soth controlled consistint flow, controlled concentration, and saturated with vapour to prevent drying the nasal mucosa. A room air blunk served as the control stimulus, EEG was recorede from 4channels according to the international 10-20 systim. Additionally, ECG, EOG, heart rate, skin conductance and resputation were recorded comtinuously. The fast Fourier transform analysis of EEG waves was analysed with the power spectra. Averaged power spectra were computed for the following frequency bands ; delta(0-4.5Hz), theta (4.5-7Hz), alphal(7-9.5Hz), alpha2 (9.5-12.5Hz) and beta(12.5-30Hz). Withthe results of the subjective sensibility test for the ordor, the orange was related to pleasant and familiar and the valeric acid was realted to snpleasant and bothersome. There is the difference between orange and valeric acid in alphal at PG2-A2 channel. While the unpleasant stimuli seem to be increased in alphal, alpha2 and beta waves at all channels. Also, the heart rate, galvaric skin resistance seem to be decreased by pleasant stimuli and thd unpleasant stimuli shdwed the opposite. In respiration, respiration rate had been declinig tendency, and input/output ampoitued and duration showed an upward trend by olfactory stimulation with orange, while opposite by valeric acid. In conclusion, the consistent EEG changes and the autonomic responses suggests the possibilities of the subjective signal of human sensibility.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.389-396
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• 1997

Several types of electrical artifact seen on electroencephalogram( EEG) records are described. Those are the EOG and the PVC roller pump noise, and so on. An adaptive digital filtering of the electroencephalogram( EEG) is a successful way of suppressing mains interference, but it affects some of the frequency components of the signal, whore artifacts may not be acceptable in some cafes of automatic EEG processing. Thus we studied the method for cancelling these artifacts. This proposed method does not use the reference channel, and is realized by connecting the linear predictor and the fixed FIR filter for the EOG artifact, and by cascading the linear predictor and the noise canceller for the pump artifact. The simulation results illustrate the performances of the proposed method in terms of the capability of interferences suppression. In the results we obtained about 20 dB noise reduction.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.38-46
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• 2024

In general, EEG signal analysis has been the subject of several studies due to its ability to provide an objective mode of recording brain stimulation, which is widely used in brain-computer interface research with applications in medical diagnosis and rehabilitation engineering. In this study, we developed EEG reception hardware to measure electroencephalograms and implemented a processing system, classifying it into server and data processing. It was conducted as an intermediate-stage research on the implementation of a brain-computer interface using electroencephalograms, and was implemented in the form of predicting the user's arm movements according to measured electroencephalogram data. Electroencephalogram measurements were performed using input from four electrodes through an analog-to-digital converter. After sending this to the server through a communication process, we designed and implemented a system flow in which the server classifies the electroencephalogram input using a convolutional neural network model and displays the results on the user terminal.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.382-387
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• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Korean Journal of Biological Psychiatry
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• v.27 no.1
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• pp.1-8
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• 2020

Objectives In electroconvulsive therapy (ECT) research and practice, the precise determination of seizure duration is important in the evaluation of clinical relevance of the ECT-induced seizure. In this study, we have developed computerized algorithms to assess the duration of ECT-induced seizure. Methods Subjects included 5 males and 6 females, with the mean age of 33.1 years. Total 55 ECT sessions were included in the analysis. We analyzed the standard deviation of a finite block of electroencephalography (EEG) data and the change in the local slope of RR intervals in electrocardiography (ECG) signals during ECT-induced seizure. And then, we compared the calculated seizure durations from EEG recording (EEG algorithm) and ECG recording (ECG algorithm) with values determined by consensus of clinicians based on the recorded EEG (EEG consensus), as a gold standard criterion, in order to testify the computational validity of our algorithms. Results The mean seizure durations calculated by each method were not significantly different in sessions with abrupt flattened postictal suppression and in sessions with non-abrupt flattened postictal suppression. The intraclass correlation coefficients (95% confidence interval) of the three methods (EEG algorithm, ECG algorithm, EEG consensus) were significant in the total sessions [0.79 (0.70-0.86)], the abrupt flattened postictal suppression sessions [0.84 (0.74-0.91)], and the non-abrupt flattened postictal suppression sessions [0.67 (0.45-0.84)]. Correlations between three methods were also statistically significant, regardless of abruptness of transition. Conclusions Our proposed algorithms could reliably measure the duration of ECT-induced seizure, even in sessions with non-abrupt transitions to flat postictal suppression, in which it is typically difficult to determine the seizure duration.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.89-94
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• 2021

In this paper, we presented the results of analysis with data obtained through EEG measurements to confirm the effect of musical stimulus when performing mathematical tasks. While the subject was solving a mathematical task, favorite and unfavorite music classified according to the subject's preference were presented as musical stimulus and the tasks were divided into memorization task and procedure task. The data measured in the EEG experiments was divided into theta waves, SMR waves and mid-beta waves which are the frequency bands related to concentration to compare the relative power spectrum values. In our results, in the case of comparing no music with favorite music and no music with unfavorite music, a significant difference was observed in the several channels, and the average difference was shown in the channels F3 and F4 of the frontal lobe. In that channels, the power was found to be greater when the music was presented than the case where there was no music. Depending on the subject's preference, it was confirmed that favorite music showed greater brain activity than unfavorite music.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.36-40
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• 2003

근래에 들어 질병으로 인하여 의사표현이 곤란한 환자에게 뇌파에 기초한 BCI(Brain Computer Interface)와 같은 새로운 인터페이스를 제공하고자 하는 연구가 활발히 진행되고 있다. BCI를 위한 기초 연구로서 특정 자극에 대해 유발되는 뇌파의 측정과 분석은 BCI를 위한 뇌파의 패턴과 인터페이스의 설계에 중요한 역할을 한다. 이 연구의 목적은 시청각 자극 인가후 피험자의 반응 시간을 측정하는 시스템을 EEG와 같은 생체 신호 계측 시스템과 연동이 가능한 형태로 개발하는 것이다. 제안된 시스템은 기능적으로 자극 신호 발생부, 반응시간 측정부, 유발뇌파 측정부, 동기신호발생부로 나뉘어진다. 자극신호 발생부는 실험에 이용되는 자극신호를 제작하는 부분으로서 Flash를 사용하여 구현하였다. 반응시간 측정부는 문제에 대한 답 선택 요청시각으로부터 피험자의 반응까지의 시간을 측정하는 부분으로서 마이크로 컴퓨터(80C31)를 이용하여 구현하였다. 우발뇌파 측정부는 시판용 하드웨어와 소프트웨어를 그대로 사용하였다. 동기신호 발생부는 전체 시스템의 동기를 맞추기 위한 신호를 발생하는 부분으로서 문제제시, 답요구와 동기한 화면상의 명암 신호와 이를 검출하는 광센서로 구성하였다. 본 논문에서 제시한 방법에서는 기존의 유발진위 측정 및 자극시스템에 특정 모듈(반응시간 측정 장치, 동기신호 발생장치)만을 추가하여 실험자의 의도에 맞는 시스템을 설계할 수 있어 유발 뇌파 및 반응시간 측정을 필요로 하는 연구를 가속화 할 것이 기대된다.