• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.178-186
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• 1996

We develop a simple adaptive population coding model for neural networks based upon an error minimization method. Our model exhibits properties that have been experimentally observed in the population coding of the motor-cortical cells during the voluntary arm movements of primates. By removing a group of directionally tuned cells after learning, we study its contribution to the population coding. Through the learning process of the remained cells, we observe how the cells modify their preferred directions to reduce the coding errors. Since this adaptive property has been neither predicted nor experimentally observed before, it would be interesting to find whether a similar adaptive property exists in real cortices that are believed to encode the information in their cell populations.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.167-171
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• 2007

Brain computer interface is the technology of interface for next generation. Recently, user intention has been tried to be recognized for interfacing a computer. EEG plays important role in developing practical application in this area. Much research has focused on extracting EEG commander generated by human movement. ERD/ERS has generally accepted as important EEG parameters for prediction of human movement. However, There has been difference between initial movement indicated by ERD/ERS and real movement. Therefore, this study was to determine the time differences for brain interface by ERD/ERS. Five university students performed ten repetitive movements. ERD/ERS was determined according to movement execution and the significant pattern showed the difference between movement execution and movement indication of ERD/ERS.

• Computational Structural Engineering
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• v.9 no.2
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• pp.93-101
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• 1996

An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

• Journal of Biomedical Engineering Research
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• v.40 no.5
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• pp.189-196
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• 2019

We investigated differences in ventricular and hippocampal volumes between CSF tap test (CSFTT) responders and non-responders in idiopathic normal-pressure hydrocephalus (INPH) patients and compared these parameters in INPH patients with that of age- and gender-matched healthy controls. We also evaluated relationships between ventricular and hippocampal volumes and clinical profiles in INPH patients. We enrolled 48 patients with INPH and 29 healthy controls. Ventricular and hippocampal volumes were measured on MRI, including 3-dimensional volumetric images. INPH patients, when compared to healthy controls, had significantly larger ventricular and smaller hippocampal volumes. No difference in ventricular and hippocampal volumes was found between CSFTT responders and non-responders in INPH patients. And hippocampal volumes showed significant negative correlations with Clinical Dementia Rating Scale scores, INPH grading scale cognitive scores, Timed Up and Go Test scores, and Unified Parkinson's Disease Rating Scale motor scores in INPH patients. Volumetric assessment of ventricular and hippocampal regions may have no predictive value in differentiating between CSFTT responders and non-responders in INPH patients. Our findings may help us understand the potential pathophysiology of unique symptoms associated with INPH.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.655-664
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• 2018

Retina transforms the external light into electrical signal that stimulates visual cortex of the brain. Electrical modeling of the retina is useful to understand its structure and action that is a prerequisite to implement the retina as a hardware device. This paper introduces a 2-D electrical network model of vertebrate's retina considering signal pathway of retinal cells and synapses. We implemented a simulator of the retina based on the electrical network model to analyze its operation under various circumstances. Compared to the prior studies, It might contribute designing of artificial retina device in terms of that this study specifically observed input and output reactions of each cell and synapse node under various light intensity on the retina.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.107-112
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• 2020

With the importance of creative learning highly valued, the demand for education in early childhood and adolescence has been increasing in recent years, but simple memorization-oriented and classical teaching methods tend not to prove high effectiveness in terms of learner-centeredness. Students who study static at their desks for a long time do not prefer boring classical learning methods, and there is also a lack of educational methods and educational content that conforms to the convergence education trend in the actual educational field. Therefore, this study has created a system that allows students to exercise and learn at the same time through a fun and familiar approach, and implement educational content through activation of brain plasticity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2143-2149
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• 2007

Visual Cortex Stimulator is among artificial retina prosthesis for blind man, is the method that stimulate the brain cell directly without processing the information from retina to visual cortex. In this paper, we propose image construction and recognition model that is similar to human visual processing by recognizing the feature data with orientation information, that is, the characteristics of visual cortex. Back propagation algorithm based on Delta-bar delta is used to recognize after extracting image feature by Kirsh edge detector. Various numerical patterns are used to analyze the performance of proposed method. In experiment, the proposed recognition model to extract image characteristics with the orientation of information from retinal cells to visual cortex makes a little difference in a recognition rate but shows that it is not sensitive in a variety of learning rates similar to human vision system.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.343-349
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• 2018

Electroencephalograph(EEG) information, which is an important data of brain science, reflects various levels of information from the molecular level to the behavior and cognitive stages, and the explosively amplified information is provided at each stage. Therefore, EEG information is an intrinsic privacy area of an individual, which is important information to be protected. In this paper, we apply spring security to web based system of spring MVC (Model, View, Control) framework to build independent and lightweight server system with powerful security system. Through the proposal of the platform type EEG analysis system which enhances the security function, the web service security of the EEG information is enhanced and the privacy of the EEG information can be protected.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.375-381
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• 2022

Brain-computer interface (BCI) is a technology that allows users with motor disturbance to control machines by brainwaves without a physical controller. Motor imagery (MI)-BCI is one of the popular BCI techniques, but it needs a long calibration time for users to perform a mental task that causes high fatigue to the users. MI is reported as showing a similar neural mechanism as motor execution (ME) and motor observation (MO). However, integrative investigations of these three tasks are rarely conducted. In this study, we propose a new paradigm that incorporates three tasks (MI, ME, and MO) and conducted a comparative analysis. For this study, we collected Electroencephalograms (EEG) of motor imagery/execution/observation from 28 healthy subjects and investigated alpha event-related (de)synchronization (ERD/ERS) and classification accuracy (left vs. right motor tasks). As result, we observed ERD and ERS in MI, MO and ME although the timing is different across tasks. In addition, the MI showed strong ERD on the contralateral hemisphere, while the MO showed strong ERD on the ipsilateral side. In the classification analysis using a Riemannian geometry-based classifier, we obtained classification accuracies as MO (66.34%), MI (60.06%) and ME (58.57%). We conclude that there are similarities and differences in fundamental neural mechanisms across the three motor tasks and that these results could be used to advance the current MI-BCI further by incorporating data from ME and MO.