• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.257-263
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• 2016

Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.5
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• pp.791-796
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• 2013

Advent of the brain wave health care system is considered as an important issues in the industrial and research area in these days. It is necessary to detect EEG signals in real-time in order to support the medical emergency service for the epileptic or brain infarct patients. Since the efficient network support is an essential factor for the system, several topologies using sensor node based wireless body area network is suggested and simulated in this paper. Finally the Opnet simulation result is evaluated for the efficient topology of the body area network.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.9
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• pp.923-928
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• 2016

The various technologies related to the monitoring human health in real-time for the emergency situations are developing these days. Mostly the human pulse is used for measuring as the vital signs so far, but the EEG became a major research trend now. However, there are some problems measuring and sending EEG signals of all the people walking down the street to the dedicated server. Especially, there are some restrictions for collecting and sending EEG signals in 2-dimensional space in real-time. Therefore, I suggests an efficient network model using 3-dimensional space of drones to avoid the restrictions. The models are designed, simulated, and evaluated with the Opnet simulator.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.1049-1061
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• 2018

As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users' brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user's content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.513-514
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• 2016

Recently, many researches has performed on human brain wave actively. In order to analyze these brain waves using EEG(electroencephalography) sensors collect EEG data and EEG can be analyzed by using a frequency analysis of the EEG. In this paper, we performed EEG analysis that NeuroSky's mindwave mobile EEG sensor collects brain wave data and analyze the delta, theta, alpha, SMR, beta wave using a frequency analysis of collected EEG. Target of this study is analysis of what kind of relationship between concentration and brain wave in frequency domain. By these analysis, we can analyse not only the commonly known close relationship between concentration and beta wave but also analyse correlation of other frequency components. Furthermore our research result will be contribute to studies to be more advanced form of brain wave analysis.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.237-242
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• 2020

Construction noise is among the most critical stressors that adversely affect the quality of life of the people residing near construction sites. Many countries strictly regulate construction noise based on sound pressure levels, as well as timeslots and type of construction equipment. However, individuals react differently to noise, and their tolerance to noise levels varies, which should be considered when regulating construction noise. Although studies have attempted to analyze individuals' stress responses to construction noise, the lack of quantitative methods to measure stress has limited our understanding of individuals' stress responses to noise. Therefore, the authors proposed a quantitative stress measurement framework with a wearable electroencephalogram (EEG) sensor to decipher human brain wave patterns caused by diverse construction stressors (e.g., worksite hazards). This present study extends this framework to investigate the feasibility of using the wearable EEG sensor to measure individuals' emotional stress responses to construction noise in a laboratory setting. EEG data were collected from three subjects exposed to different construction noises (e.g., tonal vs. impulsive noises, different sound pressure levels) recorded at real construction sites. Simultaneously, the subjects' perceived stress levels against these noises were measured. The results indicate that the wearable EEG sensor can help understand diverse individuals' stress responses to nearby construction noises. This research provides a more quantitative means for measuring the impact of the noise generated at a construction site on neighboring communities, which can help frame more reasonable construction noise regulations that consider various types of residents in urban areas.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.27-39
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• 2018

In this paper, we describe the algorithms, EEG classification methods, and position data analysis methods using EEG and ADS1299 sensors. In addition, it is necessary to manage the amount of real-time data of location data and EEG data and to extract data efficiently. To do this, we explain the process of extracting important information from a vast amount of data through a cloud server. The electrical signals extracted from the brain are measured to determine the psychological state and health status, and the measured positions can be collected using the position sensor and triangulation method.