• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7508-7512
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• 2015

We propose a brain-machine interface(BMI) based human-robot interaction(HRI) platform which operates machines by interfacing intentions by capturing brain waves. Platform consists of capture, processing/mapping, and action parts. A noninvasive brain wave sensor, PC, and robot-avatar/LED/motor are selected as capture, processing/mapping, and action part(s), respectively. Various investigations to ensure the relations between intentions and brainwave sensing have been explored. Case studies-an interactive game, on-off controls of LED(s), and motor control(s) are presented to show the design and implementation process of new BMI based HRI platform.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.7-13
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• 2003

• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.129-138
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• 2022

With the increasing reliance of computing systems in our everyday life, there is always a constant need to improve the ways users can interact with such systems in a more natural, effective, and convenient way. In the initial computing revolution, the interaction between the humans and machines have been limited. The machines were not necessarily meant to be intelligent. This begged for the need to develop systems that could automatically identify and interpret our actions. Automatic gesture recognition is one of the popular methods users can control systems with their gestures. This includes various kinds of tracking including the whole body, hands, head, face, etc. We also touch upon a different line of work including Brain-Computer Interface (BCI), Electromyography (EMG) as potential additions to the gesture recognition regime. In this work, we present an overview of several applications of automated gesture recognition systems and a brief look at the popular methods employed.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.40-48
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• 2009

Leading of the computer, IT technology has make great strides. As a information-industry-community was highly developed, user's needs to convenience about intelligence and humanization of interface is being increase today. Nowadays, researches with are related to BCI are progress put the application-technology development first in importance eliminating research about fountainhead technology with DB construction. These problems are due to a BCI-related research studies have not overcome the initial level, and not toward a systematic study. Brain wave are collected from subjects is a signal that the signal is appropriate and necessary in the experiment is difficult to distinguish. In addition, brain wave that it's not necessary to collect the experiment, serious eyes flicker, facial and body movements of an EMG and electrodes attached to the state, noise, vibration, etc. It is hard to collect accurate brain wave was caused by mixing disturbance wave in experiment on the environment. This movement, and the experiment of subject impact on the environment due to the mixing disturbance wave can cause that lowering cognitive and decline of efficiency when embodied BCI system. Therefore, in this paper, we propose an accurate and efficient brain-wave DB building system that more exactness and cognitive basis studies when embodied BCI system with brain-wave. For the minimize about brain wave DB with mixing disturbance, we propose a DB building method using an automatic control and prevent unnecessary action, put to use the subjects face tracking.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.227-236
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• 2007

Ubiquitous systems have grown explosively over the few years. Nowadays users' needs for high qualify service lead a various type of user terminals. One of various type of user interface, various types of effective human computer interface methods have been developed. In many researches, researchers have focused on using brain-wave interface, that is to say, BCI. Nowadays, researches which are related to BCI are under way to find out effective methods. But, most researches which are related to BCI are not centralized and not systematic. These problems brought about ineffective results of researches. In most researches related in HCI, that is to say - pattern recognition, the most important foundation of the research is to build correct and sufficient DB. But there is no effective and reliable standard research conditions when researchers are gathering brain-wave in BCI. Subjects as well as researchers do not know effective methods for gathering DB. Researchers do not know how to instruct subjects and subjects also do not know how to follow researchers' instruction. To solve these kinds of problems, we propose effective brain-wave DB building system using the five senses stimulation. Researcher instructs the subject to use the five senses. Subjects imagine the instructed senses. It is also possible for researchers to distinguish whether brain-wave is right or not. In real time, researches verify gathered brain-wane data using spectrogram. To verify effectiveness of our proposed system, we analyze the spectrogram of gathered brain-wave DB and pattern. On the basis of spectrogram and pattern analysis, we propose an effective brain-wave DB building method using the five senses stimulation.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.275-280
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• 2015

Recently brainwaves are utilized diversely in the field of medicine, entertainment, education and so on. In the case of medicine, brainwaves are analyzed to estimate patients' diseases. However, the applications for entertainments usually utilize brainwaves as control signal without figuring out the characters of the brainwaves. Given that users' brainwaves are different each other, a normalization method is essential. The traditional brainwave normalization approaches utilize normal distribution. However, those approaches assume that brainwaves are collected enough to conduct normal distribution. When the few amounts of brainwaves are measured, the accuracy of the control signal based on the measured brainwaves becomes low. In this paper, we propose a normalization framework of BCI-based facial interfaces for novel volume controllers, which can normalizes the few amounts of brainwaves and then generates the control signals of BCI-based facial interfaces. In the experiments, two subjects were involved to validate the proposed framework and then the normalization processes were introduced.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.112-124
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• 2010

Studies that can find resolutions to problems of subjective psychiatric analysis must be performed and indeed they are in the process. However there still lies many problems in researches of mentality examination, which should be the foundation of finding potential resolutions. One of the biggest problems in the conventional system is that there are many different opinions by psychiatrists depending on their educations and experiences. There is no objective standard on the subjects and there is no effective psychiatric analysis method. Also, the characteristic of such examinations is that it cannot be applied to disabilities, foreigners and infants alyce the examination is ch examinconversation. The objective of this atudy is to standardize TAT(Thematic Apperception Test)analysiBallken index method so that subjective data from the examination can be excluded and the examination thus maklysithe examination objectified. Furthermore, objective result and patients' brain wave pattern is read with BCI(Brain Computer Interface) ch exaTherenvironment to Alsare it to brain wave characteristics vectors to reate brain-wave characteristics vector DB. Therefore, such DB can be utilize durlysithe examination and diagnosis to reate objective examination method and standardized diagnosis system. Thus, mentality examination can be performed only with brain-wave scans with BCI based TAT system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.277-282
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• 2015

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.247-249
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• 2020

In the era of the 4th industrial revolution, the demand for convergence between ICT technologies is increasing in various fields. Accordingly, a new term that combines data, network, and artificial intelligence technology, DNA (Data, Network, AI) is in use. and has recently become a hot topic. DNA has various potential technology to be able to develop intelligent application in the real world. Therefore, this paper introduces the reviewed papers on the service image placement mechanism based on the logical fog network, the mobility support scheme based on machine learning for Industrial wireless sensor network, the prediction of the following BCI performance by means of spectral EEG characteristics, the warning classification method based on artificial neural network using topics of source code and natural language processing model for data visualization interaction with chatbot, related on DNA technology.