• Journal of Computing Science and Engineering
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• 제7권2호
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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.

• 대한의용생체공학회:의공학회지
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• 제26권4호
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• pp.231-241
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• 2005

New methods to register multiple hemispheric slices of the postmortem brain to anatomically corresponding in-vivo MRI slices within a 3D volumetric MRI are presented. Gel-embedding and fiducial markers are used to reduce geometrical distortions in the postmortem brain volume. The registration algorithm relies on a recursive extraction of warped MRI slices from the reference MRI volume using a modified non-linear polynomial transformation until matching slices are found. Eight different voxel similarity measures are tested to get the best co-registration cost and the results show that combination of two different similarity measures shows the best performance. After validating the implementation and approach through simulation studies, the presented methods are applied to real data. The results demonstrate the feasibility and practicability of the presented co­registration methods, thus providing a means of MR signal analysis and histological examination of tissue lesions via co­registered images of postmortem brain slices and their corresponding MRI sections. With this approach, it is possible to investigate the pathology of a disease through both routinely acquired MRls and postmortem brain slices, thus improving the understanding of the pathological substrates and their progression.

• 혜화의학회지
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• 제10권1호
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• pp.365-374
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• 2001

The brain is one of the most complex systems in nature. Brain waves, or the "EEG", are electrical signals that can be recorded from the brain, either directly or through the scalp. The kind of brain wave recorded depends on the behavior of the animal, and is the visible evidence of the kind of neuronal (brain cell) processing necessary for that behavior. But, EEG had been considered as a virtually infinite-dimensional random signal. However, nonlinear dynamics light on dynamical aspects of the human EEG. The methods of nonlinear dynamics provide excellent tolls for the study of multi-variable, complex system such as EEG. In this study, 20 persons seperated in 2 groups were examined with EEG, one group stimulated on specific area of the sole of the foot with footbed inside the shoes. This experiment resulted in at the group stimulated on specific area of the sole of the foot correlation dimension of P4 and O1 channels increased significantly. Therefore. we obserbed that stimulation on specific area of the body had a constant effections on the specific channels.

• 제어로봇시스템학회논문지
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• 제19권6호
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Investigative Magnetic Resonance Imaging
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• 제10권1호
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• pp.26-31
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• 2006

목적 : 감각 및 운동기능에 대한 기능적 자기공명영상에서 데이터 분석 시 global scaling이 뇌 활성화 영상에 미치는 영향을 알아보고자 하였다. 대상 및 방법 : 신경학적 병력이 없는 정상 성인 피검자 4명을 대상으로 하였다. 운동기능은 오른쪽 상지를 구부렸다가 폈다가를 반복하는 운동을 시행하였고 청각기능은 1 KHz 순음자극을 시행하였다. 기능적 자기공명영상은 3.0T 자기공명영상기기(GE, Milwaukee, USA)에서 BOLD-EPI 기법을 사용하였고 데이터 분석은 SPM2를 사용하였다. 데이터 분석 시 움직임 보정, 통계적 유의 수준 등은 동일하게 한 상태에서 global scaling의 시행 전후의 뇌 활성화 영상을 획득하였다. 결과 : 오른쪽 상지운동에 대한 기능영상에서 global scaling 효과를 고려하지 않은 경우와 고려한 경우의 뇌 활성화 영상의 차이는 크지 않았다 (p<0.000001). 청각기능 검사에서는 global scaling 효과를 고려한 경우에서 고려하지 않은 경우에 비해 뇌 활성화 영상이 훨씬 크게 나타났다 (p<0.05). 결론 : 국소적 BOLD 신호의 변화가 작은 기능영상 검사에서는 데이터 분석 시 global scaling이 뇌 활성화 결과에 큰 영향을 미칠 수 있으므로 주의가 요구된다.

• 한국지능시스템학회논문지
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• 제18권5호
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• pp.605-610
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• 2008

사람과 컴퓨터의 인터페이스를 위한 방법에는 여러 가지가 있으나 보나 편리하고 몸이 불편한 사람들도 이용할 수 있도록 하기 위하여 최근에는 사람의 뇌파를 이용하여 인터페이스를 하기 위한 연구가 활발히 진행되고 있다. 따라서 세계 여러나라에서 뇌파에 대한 연구가 진행되고 있다. 하지만 아직까지 뇌파에 대한 정확한 분석이 이루어지지 못하고 있는 실정이다. 이를 위해 본 논문에서는 정확한 뇌파분석을 위한 뇌파 유발 자극방법 및 측정법을 제안하고, Fp1, Fp2, C3, C4 영역에서 뇌파를 측정하여 사람이 팔을 움직이고자 하는 상상을 할 때 ${\mu}$파와 ${\beta}$파에서 발견되는 Event-Related Synchronization(ERS), Event-Related Desynchronization(ERD)을 분석함으로써 사람의 의도를 뇌파를 통해 인지하고자 한다. 실험결과 피험자가 오른쪽 팔을 움직이고자 할 경우 왼쪽 뇌에서 ${\mu}$파 감소하고 ${\beta}$파는 증가하였으며, 왼쪽 팔을 움직이고자 한 경우 반대로 우뇌에서 ${\mu}$파가 감소하고 ${\beta}$파가 증가하는 것을 알 수 있었다.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• 한국의학물리학회지:의학물리
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• 제5권2호
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• pp.35-43
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• 1994

전자파가 일상생활에서 생체에 많은 영향을 미치는 것으로 알려져 있으나 실제로 그 영향을 정성, 정량적으로 규명하는 것은 용이하지 않다. 본 연구에서는 생체의 전기적 신호를 이용하여 2.45GHz의 전자파에 노출시킨 토끼의 두부에 대한 영향을 측정하는 방법을 검토하였다. 연구결과, 두부모델의 손상정도는 뇌전위의 연속적인 FFT 신호처리에서 뇌활성도 저하를 관찰할 수 있었으며, 특정 주파수와 강도(최소허용전력과 치사전력)에 대한 기준 설정에 필요한 요소를 제시할 수 있었다.

• 대한임베디드공학회논문지
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• 제18권2호
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• pp.81-87
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• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• 한국수산과학회지
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• 제36권5호
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• pp.442-448
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• 2003

Melanin concentrating hormone (MCH) regulating color change of fish skin was identified from brain cDNA library of Olive flounder (Paralichthys olivaceus) during the analysis of Expressed Sequence Tags (ESTs). Olive flounder MCH gene consisted of 598 nucleotides encoding 150 amino acids. Olive flounder MCH protein revealed to contain signal peptide of 19 amino acid residues, pro-MCH of 131 amino acids being processed to biologically active and mature form of hormone with 25 amino acid residues at the carboxyl terminus. A comparative structural analysis revealed that Olive flounder MCH precursor had low sequence identity with other fish species and mammalian counterparts, while the amino acid sequences of mature hormone had a relatively high identity and more conserved. RT-PCR analysis revealed that olive flounder MCH precersor gene was expressed spectically only in the brain and not in other tissues.