• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2349-2356
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• 2008

In this paper, we present the method to effectively extract and classify the EEG caused by only brain activity when a normal subject is in a state of mental activity. We measure the synchronous EEG on the auditory event when a subject who is in a normal state thinks of a specific task, and then shift the baseline and reduce the effect of biological artifacts on the measured EEG. Finally we extract only the mental task signal by averaging method, and then perform the recognition of the extracted mental task signal by computing the AR coefficients. In the experiment, the auditory stimulus is used as an event and the EEG was recorded from the three channel $C_3-A_1$, $C_4-A_2$ and $P_Z-A_1$. After averaging 16 times for each channel output, we extracted the features of specific mental tasks by modeling the output as 12th order AR coefficients. We used total 36th order coefficient as an input parameter of the neural network and measured the training data 50 times per each task. With data not used for training, the rate of task recognition is 34-92 percent on the two tasks, and 38-54 percent on the four tasks.

• Journal of the Ergonomics Society of Korea
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• v.29 no.5
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• pp.783-788
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• 2010

This study was to examine to the CNS(Central Nervous System) responses of elderly drivers at the green, yellow, and red signal types. To examine this result, the older adults over age 65 who has more than 5 years of driving experience participated this experiment. In addition, we used audio-visual film clips as the stimulus than driving simulator, because the CNS reponses are sensitive to movement such as steering wheel. While subjects were watching one car's driving among green, yellow, or red signal types at the intersection, we measured their EEG(electroencephalogram) using monopolar electrodes from Fz, Cz and Pz sites. As a result, relative sizes of beta waves were changed due to the signal type conditions, and pre- and post-time of entering the intersection at the measured sites, separately. It suggests that the elderly drivers' CNS responses were different by the signal types.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.671-677
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• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.

• Investigative Magnetic Resonance Imaging
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• v.7 no.2
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• pp.108-115
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• 2003

Purpose : To evaluate the effect of the gradient switching noise on the ECD source localization with the EEG data recorded during the MRI scan. Materials and Methods : We have fabricated a spherical EEG phantom that emulates a human head on which multiple electrodes are attached. Inside the phantom, electric current dipole(ECD) sources are located to evaluate the source localization error. The EEG phantom was placed in the center of the whole-body 3.0 Tesla MRI magnet, and a sinusoidal current was fed to the ECD sources. With an MRI-compatible EEG measurement system, we recorded the multi channel electric potential signals during gradient echo single-shot EPI scans. To evaluate the effect of the gradient switching noise on the ECD source localization, we controlled the gradient noise level by changing the FOV of the EPI scan. With the measured potential signals, we have performed the ECD source localization. Results : The source localization error depends on the gradient switching noise level and the ECD source position. The gradient switching noise has much bigger negative effects on the source localization than the Gaussian noise. We have found that the ECD source localization works reasonably when the gradient switching noise power is smaller than $10\%$ of the EEG signal power. Conclusion : We think that the results of the present study can be used as a guideline to determine the degree of gradient switching noise suppression in EEG when the EEG data are to be used to enhance the performance of fMRI.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.277-284
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• 2013

The analysis of phase synchronization characteristics from EEG signals is important for the understanding of information processing functionality in the brain network. In this paper, wavelet transformation(WT), Hilbert tansformation (HT), complex demodulation (CD) methods having time localization characteristics were applied to real evoked potential data and noise added simulation data with center frequencies corresponding to EEG bands for the estimation performance analysis of phase offset, phase changing point, and interband crosstalk. The WT is the best both in ${\delta}$, ${\theta}$, and ${\alpha}$ band signal decomposition, and in analyzing phase synchronization performance. The CD can be efficiently used in changing point detection under tolerant noise condition because of its abrupt performance degradation over noise endurance level. From experimental observations, the WT is the most suitable in phase synchronization application of EEG signal, and the CD can be affordable in restricted application such as changing point detection for higher bands than ${\delta}$. Particularly, WT and CD can be used to detect the changing instant of brain function by indirectly estimating the phase changing point.

• Korean Journal of Biological Psychiatry
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• v.2 no.2
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• pp.257-265
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• 1995

The object of this study is to apply a chaotic signal analysis method to the EEG research, especially in the aspect of neuropsychiatry, and to get some inspection of the chaotic phenomena according to the brain sites and subjects. We have acquired 21 channel EEG data and one EOG according to the international 10-20 system and calculated the correlation dimension. The subject groups are schizophrenics, bipolar disorder, major depression and normal control. They were all awoke and eye-closed. We have found no distinctive features from our experiments except temporal regions have slightly higher correlation dimension. There is also no specific distinctions between groups. We conjecture that these results are mainly because the subjects were not well controlled. EEG dimension may change in accordance with to the age, sex, medication and the time data were selected to calculate. We have also considered some conditions for a better and more objective research of chaotic analysis to EEG research. Better conditioning and standardizing the calculation of correlation dimension is necessary for the application of the chaotic analysis to neuropsychiatry.

• Journal of Acupuncture Research
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• v.19 no.3
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• pp.26-40
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• 2002

Objective : The aim of this study was to examine the effects of electroacupuncture(EA) at the PC6 (Naegwan) on nomal humans using power spectrum analysis. Methods : Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different dominant frequency ranges and different origins. EEG power spectrum exibit site-specific and state-related differences in specific frequency bands. In this study, power spectrum was used as a measure of complexity. 30 channel EEG study was carried out in 8 subjects (8 males; age=$21.4{\pm}0.5$ years). Results : We found that the power values of the alpha wave at F7 channel (P<0.05) were highered than during the acupuncture treatment, and the power values of the delta wave at TCP1, CP1, TCP2, T5 channels(P<0.05) were lowered than during the acupuncture treatment. However, the comparison with that before and after the treatment shows no significant differences in all channels.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.605-610
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• 2008

There are many methods for Human-Computer Interface. Recently, many researchers are studying about Brain-Signal this is because not only the disabled can use a computer by their thought without their limbs but also it is convenient to general people. But, studies about it are early stages. This paper proposes an EEG signals measurement and analysis methods for Brain-Computer Interface. Our purpose of this research is recognition of subject's intention when they imagine moving their arms. EEG signals are recorded during imaginary movement of subject's arms at electrode positions Fp1, Fp2, C3, C4. We made an analysis ERS(Event-Related Synchronization) and ERD(Event-Related Desynchronization) which are detected when people move their limbs in the ${\mu}$ waves and ${\beta}$ waves. Results of this research showed that ${\mu}$ waves are decreased and ${\beta}$ waves are increased at left brain during the imaginary movement of right hand. In contrast, ${\mu}$ waves are decreased and ${\beta}$ waves are increased at right brain during the imaginary movement of left hand.

• Transactions on Electrical and Electronic Materials
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• v.14 no.6
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• pp.299-303
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• 2013

Anesthesia, which started being used to remove pain during surgery, has become itself one of the major concerns to be considered during surgery. While actual anesthesia is being performed, patients tend to have unpleasant experiences, due to wakening that accompanies pain, or wakening that does not accompany pain. Since this awakening during anesthesia is a most unpleasant experience in a patient's life, evaluating the depth of anesthesia during surgery is essential for patients to avoid this experience. Although there has been much effort on the understanding and measurement of the depth of anesthesia, while various researches were performed on the need of anesthesia, the development of an indicator that could objectively evaluate the depth of anesthesia, other than by using the patient's vital signs, is still inadequate. Therefore, this study was to develop an objective indicator by using EEG and ECG, which are essentially measured during the surgery, to evaluate the depth of anesthesia. The experiment was performed by taking patients who require a relatively short operation time, and general inhalation anesthetics among surgical patients in obstetrics and gynecology as the subjects of experiment, to measure the EEG and ECG signals of patients under anesthetics. The result showed that SEF using EEG and LF, HF using ECG signal and correlation dimension analysis parameter were valuable parameters that could measure the depth of anesthesia, by the stage of anesthesia.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.305-310
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• 2006

The color and structure of urban constructions is a factor of urban landscape and shows their characteristics. Hence the modern buildings deal with their materials and external appearance as an important factor, making up the urban image. But it was nearby impossible to evaluate the value of visual landscape with objective measuring method. Most of all, it depends on the subjective estimation of a few talented or high educated experts with a sense of beauty. Such kinds of estimation can in some cases include arbitrary interpretations. In relation to this kind of problems, it is tried here in this study to analyse the human response of brain wave pattern (EEG) with use of SD method, while the tested persons watch the urban landscape scenery constructed in a visual reality. The tested persons were 20 adult male and female with no color blindness and intact cognitive function. Light source with color filter was used for color environment in a dark soundproof chamber. The signal of EEG is analysed digitally and grouped into the ${\alpha}$ and ${\beta}$ waves. The result showed that relative power of ${\alpha}$ wave ratio increased in the natural landscape scenery with blue and green color. From these results it was possible to evaluate the human response, which is affected by urban and natural color and structure stimulation and it might be useful as an indicator of visual cognition amenity toward the design of urban construction environment.