• Journal of National Security and Military Science
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• s.1
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• pp.249-267
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we are mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject's will, for the purpose of controlling the machine correctly and reliably. We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals' pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• 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 Biomedical Engineering Research
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• v.31 no.1
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• pp.1-13
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• 2010

There are a great numbers of disabled individuals who cannot freely move or control specific parts of their body because of serious neurological diseases such as spinal cord injury, amyotrophic lateral sclerosis, brainstem stroke, and so on. Brain-computer interfaces (BCIs) can help them to drive and control external devices using only their brain activity, without the need for physical body movements. Over the past 30 years, several Bel research programs have arisen and tried to develop new communication and control technology for those who are completely paralyzed. Thanks to the rapid development of computer science and neuroimaging technology, new understandings of brain functions, and most importantly many researchers' efforts, Bel is now becoming 'practical' to some extent. The present review article summarizes the current state of electroencephalogram (EEG)-based Bel, which have been being studied most widely, with specific emphasis on its basic concepts, system developments, and prospects for the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.153-158
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• 2010

This paper showed the possibilities of performance improvement of Brain-Computer Interface (BCI) decreasing classification error rates of EEG signals by applying Variance-Considered Machine (VCM) which proposed in our previous study. BCI means controlling system such as computer by brain signals. There are many factors which affect performances of BCI. In this paper, we used suggested algorithm as a classification algorithm, the most important factor of the system, and showed the increased correct rates. For the experiments, we used data which are measured during imaginary movements of left hand and foot. The results indicated that superiority of VCM by comparing error rates of the VCM and SVM. We had shown excellence of VCM with theoretical results and simulation results. In this study, superiority of VCM is demonstrated by error rates of real data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.111-117
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• 2020

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.18-23
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• 2010

In this paper, we propose a computationally efficient method detecting the P300 wave for brain-machine interface. Electrophysiological researches have shown that the P300 wave's potential is decreased when human intention matches visual stimulation. Motivated by this fact, we can infer human intention for brain-machine interface by detecting the P300 wave's potential decrease. The P300 wave is recorded from EEG(electroencephalogram) electrodes attached on human brain skull after giving alphabetical stimulation. To detect the potential decrease in P300, firstly we statistically model the P300 wave's negative potential. Then we infer human intention based on maximum likelihood estimation. The proposed method was evaluated on the data recorded from three healthy human subjects. The method achieved an averaging accuracy of 98% from subject k, 90% from subject j and 79.8% from subject h.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• 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 Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).