• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.50-54
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• 2014

It is being tried to control robot arm using brain signal in the field of brain-machine interface (BMI). This study is focused on applying guidance laws for efficient robot arm control using 3D coordinates obtained from Magnetoencephalography (MEG) signal which represents movement of upper limb. The 3D coordinates obtained from brain signal is inappropriate to be used directly because of the spatial difference between human upper limb and robot arm's end-effector. The spatial difference makes the robot arm to be controlled from a third-person point of view with assist of visual feedback. To resolve this inconvenience, guidance laws which are frequently used for tactical ballistic missile are applied. It could be applied for the users to control robot arm from a first-person point of view which is expected to be more comfortable. The algorithm which enables robot arm to trace MEG signal is provided in this study. The algorithm is simulated and applied to 6-DOF robot arm for verification. The result was satisfactory and demonstrated a possibility in decreasing the training period and increasing the rate of success for certain tasks such as gripping object.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.260-263
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• 2003

EEC is an electrical signal, which occurs during information processing in the brain. These EEG signals has 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. A BCI has to perform two tasks, the parameter estimation task, which attemps to describe the properties of the EEG signal and the classification task, which separates the different EEC patterns based on the estimated parameters. First, we have to do parameter estimation of EEG to embody BCI system. It is important to improve performance of classifier, But, It is not easy to do parameter estimation by reason of EEG is sensitivity and undergo various influences. Therefore, this research should do parameter estimation and classification of the EEG to use various analysis algorithm.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.89-98
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• 2008

NIRS (Near-infrared Spectroscopy) and DOI (Diffuse Optical Imaging) are relatively new, non-invasive, and non-ionizing methods that measure or image optical properties (Scattering and Absorption Coefficient) and physiological properties (Water Fraction, concentration of Oxy-, Deoxy-Hemoglobin, Cytochrome Oxidase, etc) of biological tissues. In this paper, three different types of NIRS systems, mathematical modeling, and reconstruction algorithms are described. Also, recent applications such as functional brain imaging, optical mammography, NIRS based BMI (Brain-Machine Interface), and small animal study are reviewed.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1912-1913
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• 2007

뇌 기계 인터페이스는 뇌에 직접 연결을 시도하는 인터페이스로서 인간의 의지 또는 생각을 컴퓨터가 인식할 수 있는 디지털 신호로 바꾸는 새로운 휴먼 컴퓨터 인터페이스 중 하나이다. 뇌신경의 신호 전달 과정이 전기적, 화학적 특성을 지닌다는 사실에 착안하여 뇌의 활동을 측정하는 많은 기술들이 개발되어 왔다. PET, fMRI, MEG, EEG 등을 포괄하는 brain functional imaging 기술 중 뇌 기계 인터페이스에서 가장 주목하고 있는 것이 바로 EEG 이다. 본 연구에서는 뇌기계 인터페이스 시스템 개발에 필요한 무선 EEG 측정 장치를 설계하고, 무선 EEG 측정 장치와 컴퓨터간에 데이터 전송과 EEG 신호를 FFT 분석 하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.156-166
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• 2012

Over the past 15 years productive BCI research programs have arisen. Current mainstream EEG electrode setups permit efficient recordings but most of electrodes has the disadventages of need for skin preparation and gel application to correctly record signals. The new gel-free probe was adapted for EEG recording and it can be fixed to the scalp with the micro needle without neuro-gel. It use standard EEG cap for wearing electrodes on scalp so it is compatible with standard EEG electrodes. A comparison between electrode characteristics is achieved by performing simultaneous recordings with the gel electrodes and gel-free probe placed in parallel scalp positions on the same anatomical regions. The quality of EEG recordings for all two types of experimental conditions is similar for gel-electrodes and gel-free probe. Subjects also reported not having special tactile sensations associated with wearing of gel-free probes. According to our results, it is expected that gel-free probe can be adapted to BCI, BMI(Brain Machine Interface), HMI(Human Machine Interface) because of its simple application and comfortable wearing process.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.725-730
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• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.199-203
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• 2016

A brain-computer interface or BCI provides an alternative method for acting on the world. Brain signals can be recorded from the electrical activity along the scalp using an electrode cap. By analyzing the EEG, it is possible to determine whether a person is thinking about his/her hand or foot movement and this information can be transferred to a machine and then translated into commands. However, we do not know which information relates to motor imagery and which channel is good for extracting features. A general approach is to use all electronic channels to analyze the EEG signals, but this causes many problems, such as overfitting and problems removing noisy and artificial signals. To overcome these problems, in this paper we used a new optimization method called the Fruit Fly optimization algorithm (FOA) to select the best channels and then combine them with CSP method to extract features to improve the classification accuracy by linear discriminant analysis. We also used particle swarm optimization (PSO) and a genetic algorithm (GA) to select the optimal EEG channel and compared the performance with that of the FOA algorithm. The results show that for some subjects, the FOA algorithm is a better method for selecting the optimal EEG channel in a short time.