• 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.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.204-215
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• 2020

People who have sleep disorders such as insomnia take a long time to get to sleep, namely sleep latency. In order to reduce it, effective stimulations and environments to induce sleep such as ASMR or pink noise are necessary. However these have different effects and preferences for each individual. Therefore customized service and control for the sleep induction will be provide to him/her. In this paper, we proposed SIS control system which provides selectively sound control among various kinds of ASMR and pink noise according to sleep state measured from brain wave data for an individual. In order to verify the effectiveness of the system, we had conducted totally 30 experiments for 5 people, and all EEG data measured from all the people during sleep. An average of 3.7 hours was spent per experiment. In comparison experiments with and without sound control for sleep induction, the latency time was reduced by an average of 8 minutes as well as delta waves and theta waves, which appear only in deep sleep, are increased by 21%.

• Journal of Magnetics
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• v.21 no.1
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• pp.148-152
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• 2016

The main goal was to evaluate effectiveness of a modified TSE sequence compared with DIR (double inversion recovery) sequence in acquisition of fast flow brain vessel images using signal void effect. 32 healthy volunteers (10 men and 22 women; mean age of 31 years; ranging between 28-43 years) who underwent black blood DIR sequence (group A) and the modified TSE sequence (group B) were enrolled in our study. Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) of the internal carotid arteries' lumen were compared in T1 and T2 weighted images for both group A and B. The images obtained from group B showed lower SNR values in internal carotid artery than the group A in both of the T1 and T2 weighted images (11.49% and 13.66% respectively). While the CNR values were higher in the group B than the group A in both of the T1 and T2 weighted images (8.69% and 7.55 % respectively). The qualitative score of all categories were not significantly different between the two groups. Furthermore approximately 49% of the total scan time was reduced from group B. Our study is to shorten the scanning time and minimize the inconveniences of the patients in acquisition of the black blood images of brain by using the signal void effect in the modified TSE technique while keeping the diagnostic value of the test.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.421-428
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• 1997

Magnetic field sensors made from superconducting quantum interference device (SQUID) are the most sensitive low-frequency sensors available, enabling measurements of extremely weak magnetic fields from the brain. Neuromagnetic measurements allow superior spatial resolution, compared with the present electric measurements, and superior temporal resolution, compared with the fMRl and PET, providing useful informations for the functional diagnoses of the brain. We developed a 4-channel SQUID system for neuromagnetic applications. The main features of the system are its simple readout electronics and compact pickup coil structure. A magnetically shielded room has been constructed for the reduction of environmental magnetic noises. The developed SQUID system has noise level lower than the magnetic noise from the brain. Magnetic field signals of the spontaneous r-rhythm activity and auditory evoked magnetic fields have been measured.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.121-128
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• 2010

This study is to analyze change of connectivity between brain positions caused by relaxation through EEG coherence. EEG spectrum analysis method has been used to analyze brain activity when relaxation was experienced. However, the spectrum analysis method has a limit that could not observe interactive reaction between brain-functional positions. Therefore, coherence between positions was analyzed to observe connectivity between the measurement positions in this study. Through the method, the reaction of the central nervous system caused by the emotion change was observed. Twenty-four undergraduates of both genders(12 males and 12 females) were asked to close their eyes and listen to the sound. During experiment, EEG was measured at eight positions. The eight positions were F3, F4, T3, T4, P3, P4, O1, and O2 in accordance with International 10-20 system. The sounds with white noise and without were used for relaxation experience. Subjective emotion was measured to verify whether or not they felt relaxation. Subjective emotion of participants were analyzed by ANOVA method(Analysis of Variance). In the result, it was proved that relaxation was subjectively evoked when participants heard sound. Accordingly, it was proved that relaxation could be enhanced by the mixed white noise. EEG coherence between the measurement positions was analyzed. T-test was performed to find its significant difference between relaxation and not-relaxation. In the results of EEG coherence, connectivity with occipital lobes has been increased with relaxation, and connectivity with parietal lobes has been increased with non-relaxed state. Therefore, brain connectivity has shown different pattern between relaxed emotion and non-relaxed emotion.

• Journal of IKEEE
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• v.14 no.2
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• pp.16-23
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• 2010

Since the interest of a brain science research is increased recently, various devices using brain waves have been developed in the field of brain training game, education application and brain computer interface. In this paper, we have developed a portable EEG measurement and a bluetooth based wireless transmission device measuring brain waves from the frontal lob simply and conveniently. The low brain signals about 10~100${\mu}V$ was amplified into several volts and low pass, high pass and notch filter were designed for eliminating unwanted noise and 60Hz power noise. Also, PIC24F192 microcontroller has been used to convert analog brain signal into digital signal and transmit the signal into personal computer wirelessly. The sampling rate of 1KHz and bluetooth based wireless transmission with 38,400bps were used. The LabVIEW programing was used to receive and monitor the brain signals. The power spectrum of commercial biopac MP100 and that of a developed EEG system was compared for performance verification after the simulation signals of sine waves of $1{\mu}V$, 0~200Hz was inputed and processed by FFT transformation. As a result of comparison, the developed system showed good performance because frequency response of a developed system was similar to that of a commercial biopac MP100 inside the range of 30Hz specially.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.582-587
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• 2002

Current information storage devices, such as HDD, CD/DVD-ROM/RW, probe-based memory and hologram memory, are compared with biological information storage mechanisms in DNA and brain memory. Newly developed approaches to overcome the limit of storage capacity are introduced in both magnetic and optical recording devices. Linear and areal density of information stored in the biological and mechanical storages are compared for the applications and developments of new storage devices.

• Progress in Superconductivity
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• v.13 no.1
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• pp.18-23
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• 2011

We developed a four-channel first order gradiometer system to measure magnetoencephalogram for mice. We used double relaxation oscillation SQUID (DROS). The diameter of the pickup coil is 4 mm and the distance between the coils is 5 mm. Coil distance was designed to have good spatial resolution for a small mouse brain. We evaluated the current dipole localization confidence region for a mouse brain, using the spherical conductor model. The white noise of the measurement system was about 30 fT/$Hz^{1/2}$/cm when measured in a magnetically shielded room. We measured magnetic signal from a phantom having the same size of a mouse brain, which was filled with 0.9% saline solution. The results suggest that the developed system has a feasibility to study the functions of brain of small animals.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.112-121
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• 2013

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.