• 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.

• Journal of IKEEE
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• v.22 no.4
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• pp.1109-1114
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• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.117-120
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• 1995

The electronencephalogram (EEG) is a complex electrical signal which reflects generalized brain activity. The EEG is utilized in the clinical assesment of many neurological and psychiatric disorders and offers promise for monitoring of patients undergoing operation. This paper describes a technique for quantitative analysis of EEG signals which is based on an eigenspace. Examples of the application approach to simulated and clinical EEG data illustrate the capabilities.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.1-8
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• 2019

Motion sickness is dizziness symptom that occurs when movement detected in the vestibular organ and movement detected visually are collide with each other. When dizziness occurs, user complains of symptoms such as nausea and vomiting, sense of direction abnormality, and fatigue. These causes of dizziness are various and difficult to differentiate and treat the symptoms. Especially, among the types of dizziness VIMS(Visually Induced Motion Sickness) is a problem to solve in developing VR industry. These VIMS analysis can be done through user's vital signs measurement and feature analysis, and EEG characteristics analysis. Therefore, this paper is discuss the minimization of motion sickness caused by visual information based on EEG signal and present research trends related to it.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.642-645
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.824-836
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• 2020

This study investigated the effects of textile attachments on electroencephalogram using 20 persons (10 males and 10 females). Four types of attachment cushions were manufactured by changing the shell fabric (cotton and microfiber) and interlining (synthetic loose fiber and buckwheat). This was done using BIOS-S8 (BioBrain Inc., Korea), an 8-channel polygraph for multi-body signal measurement, to measure EEG. Data were analyzed using the SPSS 24.0 statistical program. EEG values were significantly activated according to gender, particularly when the subjects' eyes were open. For the male cases, 'RT', 'RAHB' values were highly activated and for the female cases, 'RA', 'RB', 'RG', 'RFA', 'RST', 'RLB', 'RMB', 'RST', 'RMT' values were highly activated. Examining the differences in EEG according to type of attachment indicated no significant difference in both sexes. However, in cases of females with their eyes closed, the 'RSA' index was quite different in the left occipital lobe (O1), and when their eyes were open, the 'RFA' in the right frontal lobe (F4) showed a significant difference. However, there was no obvious correlation between the activation of EEG and the subjective preference of textile attachments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.260-263
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• 2003

EEC is an electrical signal, which occurs during information processing in the brain. These EEG signals has been used clinically, but nowadays we are mainly studying Brain-Computer Interface(BCI) such as interfacing with a computer through the EEG controlling the machine through the EEG The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. A BCI has to perform two tasks, the parameter estimation task, which attemps to describe the properties of the EEG signal and the classification task, which separates the different EEC patterns based on the estimated parameters. First, we have to do parameter estimation of EEG to embody BCI system. It is important to improve performance of classifier, But, It is not easy to do parameter estimation by reason of EEG is sensitivity and undergo various influences. Therefore, this research should do parameter estimation and classification of the EEG to use various analysis algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.595-600
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• 2014

Nowadays our society is rapidly transforming into an aging society. A better understanding of dementia is a high priority in the aging society. Therefore our study is basically aimed at understanding characteristics of EEG signals from dementia patients. Firstly, we analyzed spontaneous EEG signals from normal persons and dementia patients to distinguish their characteristics. The EEG signals are recorded with 16 electrodes and we classified the EEG signals form the signals according to frequency band. To obtain the clean EEG signals, we used cross correlation function between two channels. From the analysis results, we can observe that the EEG characteristics from dementia patients are distinctly different from that from normal persons.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2170-2178
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• 2014

In this paper, electroencephalographic (EEG) signal of one among subjects measured biosignal with visual evoked stimuli inducing the concentration was analyzed to detect the changes in the attention status during attention task fulfillment from January to February, 2011. The independent component analysis (ICA) was applied to EEG signals to isolate the attention related innate source signal within the brain and Electroculogram (EOG) artifact from measured EEG signals at the scalp. The consecutive accumulation of short time Fourier transformed (STFT) attention source signal with excluded EOG artifact can enhance the regular depiction of EPOCH graph and spectral color map representing time-varying pattern. The extracted attention indices associated with somatosensory rhythm (SMR: 12-15 Hz), and theta wave (4-7 Hz) increase marginally over time. Throughout experimental observation, the ICA with STFT can be used for the assessment of participants' status of attention.