• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.412-421
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• 2023

Ultrasound brain stimulation is spot-lighted by its capability of inducing brain cell activation in a localized deep brain region and ultimately treating impaired brain function while the efficiency and directivity of neural modulation are highly dependent on types of stimulus waveforms. Therefore, to optimize the types of stimulation parameters, we propose a cell-cultivable ultrasonic transducer having a series stack of a spin-coated polymer piezoelectric element (Poly-vinylidene fluoride-trifluorethylene, PVDF-TrFE) and a parylene insulating layer enhancing output acoustic pressure on a glass-coverslip which is commonly used in culturing cells. Due to the uniformity and high accuracy of stimulus waveform, tens of neuronal cell responses located on the transducer surface can be recorded simultaneously with fluorescence microscopy. By averaging the cell response traces from tens of cells, small changes to the low intensity ultrasound stimulations can be identified. In addition, the reduction of stimulus distortions made by standing wave generated from reflections between the transducers and other strong reflectors can be achieved by placing acoustic absorbers. Through the proposed ultrasound transducer, we could successfully observe the calcium responses induced by low-intensity ultrasound stimulation of 6 MHz, 0.2 MPa in astrocytes cultured on the transducer surface.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.416-418
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• 2003

해마 뉴런의 시냅스에서 발생하는 전류는 후시냅스의 생화학적 반응을 통해 다음 뉴런으로 전달된다. 즉, 시냅스는 정보를 전달하는 매개로서 전시냅스에서 입력된 정보에 의거하여 후시냅스로 보내는 전류량을 조절하게 된다. 본 논문에서 제안하는 시냅스 기전 신경망 모델은 기존의 신경망과는 달리 시냅스에서 일어나는 반응-확산(reaction-diffusion) 모델에 의하여 입력과 출력의 관계를 결정한다. 제안된 신경망을 분류 문제에 적용한 결과 은닉 뉴런층 없이도 좋은 성능을 보였으며, 이 신경망은 앞으로 뇌에서의 생화학적 뉴런 학습 양상을 연구하는 모델로 사용될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1100-1102
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• 2017

손가락 운동은 뇌에 가장 큰 영향을 미친다고 알려져 있으며, 손의 기능은 운동과 감각의 복합 기능을 가지고 있기 때문에 식사하기, 옷 입기 등 일상생활을 하는데 있어 반드시 필요하다. 본 연구는 만성 뇌졸중 환자를 대상으로 손가락 재활치료를 위해 환자의 손동작 인식을 위한 'Real Sense'와 게임 개발 엔진인 'Unity3D'를 연동하여 게임을 개발하는 것을 목적으로 한다. 제안하는 게임은 활동성을 부가함으로써 손가락 재활이라는 특정 목적을 달성하는 기능성 게임이며, 주어진 과제를 단계별로 나누어 진행하도록 하여 난이도를 설정하였다. 우리는 환자들의 게임 참여도를 높이기 위해 딱딱한 화면이 아닌 친숙한 게임형식으로 구성하여 환자들이 지루함 없이 자발적으로 재활치료를 할 수 있도록 도움을 주며, 환자들은 우리의 게임을 이용하며 손가락을 균형 있게 사용함으로써 뇌 활동을 향상시킬 수 있다. 기존의 재활치료는 환자가 직접 병원을 가야하는 불편함과 가격이 비싼 재활 치료 기계를 사야하는 반면에 본 연구에서는 비교적 저렴하고 가벼운 'Real Sense'를 이용하여 시간과 공간에 얽매이지 않고 재활치료를 할 수 있도록 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.635-637
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• 2017

The perception that programming is difficult is spread among learners. Indeed, in college education, the dropout rate of programming classes is higher than in other courses. Therefore, it is necessary to analyze the cognitive aspects of why learners think programming is difficult and then to propose appropriate teaching strategies for them. Recently, studies are under way to understand how the brain learns and is most effective in what situations, based on the development of brain science. This is the study of brain-compatible learning. The purpose of this study is to propose an instructional design on programming lessons based on brain-compatible learning principles.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.508-514
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• 2018

An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.

• The Journal of Korean Association of Computer Education
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• v.13 no.6
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• pp.35-42
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• 2010

In this paper, the characteristics of online game addiction have been analyzed using fMRI. The fMRI images are taken from six target subjects who are around 20 years old, right-handed, and undergraduate male students with online game stimulations. The images are processed using SPM5, and statistical analysis showed following characteristics. First, online game stimuli produces an activation in BA18 of brain, and the Pearson correlation coefficient between the activation intensity of BA18 area and the addiction index value is very highly as .94. Second, the Pearson correlation coefficient is .75 between addiction index of subjects and activation index of the mesencephalon. From these observations, we found that the online game stimuli were processed as visual stimuli by subjects' brain, and the subject's brain with bigger addiction index processes more actively from the online game stimuli. Also, the online game stimuli activate the mesolimbic system, and therefore these findings may contribute for comparing the mechanism between general addiction and online game addiction.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.603-611
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• 2022

As the era of metaverse approaches, there are challenges that need to be solved. Among them, 'cyber motion sickness' is a representative problem from 2016;when VR technology began to attract attention. According to the theory of sensory conflict, motion sickness is caused when the perceived direction of motion information and the expected value are not the same. The paper was written to theoretically explore the relationship between brain cognition and cyber motion sickness, and to prove the effect of user immersion on motion sickness symptoms based on this. Through the SSQ experiment, it was found that the rotation value of the camera aggravates the symptoms of cyber motion sickness and can alleviate cyber motion sickness by increasing the immersion of the game by giving the viewer visual and shift missions to solve. This study was conducted to solve the problem of cyber motion sickness during the process of developing the VR rhythm game "beatale", and it is expected to be the basis for improving cyber motion sickness not only in the development of the project but also in the production of VR contents in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.379-382
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• 2011

인터넷 뱅킹, 전자 상거래 등의 도래에 따라 생체 인식이 중요한 이슈가 되고 있다. 이에 따라 뇌전도(Electro Encephalo Graphy: EEG)로 측정되는 생체 신호를 통하여 기존 생체 인식의 단점을 보완하는 새로운 연구가 시도되고 있다. 본 논문에서는 인간 본인의 얼굴 사진에 특별한 반응을 보인다는 신경 생리학적 지식을 기반으로 한, 새로운 개인 인증 기술을 제안한다. 구체적으로는 뇌 신호 반응 유도를 위한 시각 자극 제시 패러다임의 설계 EEG신호의 특징을 추출을 위한 개인-의존적인 시간 영역 및 채널 선택 및 효율적인 분류기 설계 방법을 제안한다. 제안한 방법을 이용한 실험 결과는 EEG 기반의 개인 인증 및 인식의 가능성을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.357-358
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• 2023

본 연구는 실제 행동과 운동 심상으로 팔과 다리 동작 인식을 위한 BCI 패러다임을 제안하고 유도성 분석을 한다. 이 페러다임은 각 팔과 양다리의 특정 움직임을 인식하기 위해 ERP를 기반 페러다임을 구성한다. BCI 페러다임은 왼팔, 오른팔, 양다리를 움직이는 영상 자극을 주며 이를 기반으로 왼팔, 오른팔, 양다리 움직임에 대한 인식을 한다. 거울뉴런은 실제 행동과 실제 행동을 보았을때와 운동심상을 통한 자극을 받았을 때 같은 뉴런이 활성화된다는 성질을 가지고 있다. 이러한 성질을 이용하여 운동심상만과 실제 행동을 동시에 학습할 경우를 유도성 분석을 진행한다. 또한 유도성 특징 분석을 통해 나타난 결과를 바탕으로 BCI 패러다임을 제안한다.