• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1395-1396
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• 2011

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.39-40
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• 2011

• Clinical and Experimental Pediatrics
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• v.46 no.2
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• pp.137-142
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• 2003

Purpose : This study was performed to determine the sensitivity of neonatal electroencephalography (EEG) in detecting underlying brain disease, to compare the sensitivity and specificity of EEG with those of brain ultrasonography and to determine the prognostic value of EEG for neonatal neurologic diseases. Methods : Eighty-seven newborn babies were subjected to a electroencephalographic examination for the evaluation of underlying neurological diseases and EEGs were recorded at least before three days of life. The findings of early ultrasonography performed within three days after birth were compared with those of magnetic resonance imaging(MRI) or ultrasonography after seven days of life. Results : The EEG results were more sensitive and specific than ultrasonography for the detection of neonatal brain damage. The EEG results showed 91.7% sensitivity for mild grade neurological sequelae and 100.0% sensitivity for moderate and severe-grade neurological sequelae in predicting the neurological outcome. However, early ultrasonography results showed 20.8% and 18.8% of sensitivity and specificity, respectively. Conclusion : EEG is a highly sensitive diagnostic tool for detecting neonatal brain disease and is valuable for predicting the long-term outcome of neurologic sequelae.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.924-926
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• 2015

In this paper, we use the interface, which many countries concentrates research of Brain - Computer Interface with the device and MyndPlay based on the IoT intelligent Arduino. Finally we will make the Brain - Computer Connection environment, the purpose of Brain - Computer Interface. Recognizes the EEG of a person who wearing the equipment, analyze, classify, and we did a research to design an intelligent thing to suit user's condition. In addition, we use the XBee, and Bluetooth to communicate to other devices, such as smart phone. In conclusion, this paper check users current status via brain waves, and it allows to control the power and other objects by using the EEG(Electroencephalography).

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• Journal of Korean Society on Water Environment
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• v.39 no.3
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• pp.223-230
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• 2023

Harmful algae blooms have become a serious environmental problem in major river basins in Korea. They are known to produce various algal organic matters (AOMs) including intracellular organic matters (IOMs) and extracellular organic matters (EOMs). Generally AOMs cannot be easily removed by coagulation/flocculation process in conventional drinking water plants. AOMs produced by blue-green algae also include various toxins such as Microcystins, Anatoxin-a, and Saxitoxin known to have harmful effects on living organisms in aquatic environment. In this study, toxic effects of EOMs produced by three different algae species (Microcystis sp., Anabaena sp., and Oscillatoria sp.) on zebrafish were investigated using electroencephalography (EEG) recording method, a technology for recording brain activity. Electroencephalographic changes in zebrafish revealed that a low EOM had a negative effect on zebrafish compared to both Anabaena sp. and Oscillatoria sp. at 30 ppm EOM exposures. This result might be due to Microcystins present in EOMs produced by Microcystis sp. As a result of power spectrum density anallysis, exposure to EOMs produced by Microcystis sp. caused a state of vigilance in zebrafish. This EEG based toxicity test can be used to examine effects of harmful materials at low levels on living organisms in an aquatic system.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1179-1187
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• 2023

Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1819-1824
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• 2018

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.

• Journal of Veterinary Clinics
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• v.23 no.1
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• pp.31-35
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• 2006

This study was performed to evaluate sedation with quantitative electroencephalography (EEG) analysis in dogs. EEG is used to evaluate objectively the effects of CNS acting with brain and behavioral changes. Especially, spectral edge frequency 95 (SEF 95) parameter is an effective method to determine the sedative status. The SEF 95 is the frequency below 95% of the total power. Twelve healthy intact male Miniature Schnauzer dogs, which did not show any neurological abnormalities and disease, were used for the study. EEG electrodes were inserted in subcutaneous tissue over the calvaria without entering adjacent muscles. The EEG data were acquired and analyzed by EEG raw wave and spectral edge frequency 95 analysis. After the administration of sedatives, the SEF 95 values were shown the significant changes compared with the normal state In all groups (p<0.05). It is suggested that SEF 95 analysis is useful method for assessing the state of sedation in dogs.