• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.63-70
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• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.31-38
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• 2016

The brain and muscles both of which are composed of top-down structure occur the connectivity with the change of Electroencephalogram(EEG) and Electromyogram(EMG). In this paper, we studied the difference of functional connectivity between brain and muscles that by applying coherence method to EEG and EMG signals when users exercised upper limb with and without the movement intention. The changes in the EEG and EMG signals were inspected using coherence method. During the upper limb exercise, the mu (8~14 Hz) and beta (15~30 Hz) rhythms of the EEG signal at the motor cortex area are activated. And then the beta and piper (30~60 Hz) rhythms of the EMG signal are activated as well. The result of coherence analysis between EEG and EMG showed the coefficient of active exercise including movement intention is significantly higher than passive exercise. The coherence relations between cognitive response and muscle movement could interpret that the connectivity between the brain and muscle appear during active exercise with movement intention. The feature of coherence between brain and muscles by the status of movement intention will be useful in designing the rehabilitation system requiring feedback depending on the users' movement intention status.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.330-334
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• 2009

본 연구는 뇌의 편측 영역 에서의 EEG(Electroencephalography) coherence 로 손동작 의도를 예측하고자 하는 연구이다. 손 동작 예측을 위한 실험에 신체에 이상이 없는 6 명의 피실험자가 참여 하였다. 실험은 데이터 트레이닝 6 분과 동작 의도 판단 6 분으로 진행되었으며 무작위 순서로 손 동작을 지시한 후 편측적 영역 5 개 지점의 EEG 와 동작 시점을 알기 위한 오른손 EMG(Electromyography)를 측정하였다. 측정된 EEG 데이터를 분석하기 위해 주파수 별 Alpha 와 Beta 를 분류하였고 EMG 신호를 기준으로 동작과 휴식으로 분류된 Alpha 와 Beta 데이터를 5 개의 측정 영역별 Coherence 분석을 하였다. 그 결과 동작과 휴식을 구분할 수 있는 통계적으로 유효한 EEG Coherence 영역을 통하여 동작 판단을 할 수 있음을 확인하였다.

• Journal of IKEEE
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• v.22 no.4
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• pp.1109-1114
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• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.289-292
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• 2009

A person does communication between each other using language. But In the case of disabled person can not communication own idea to use writing and gesture. Therefore, In this paper, we embodied communication system using the facial muscle signals so that disabled person can do communication. Especially, After feature extraction of the EEG included facial muscle, it is converted the facial muscle into control signal, and then select character and communicate using a minimum list keyboard.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.832-837
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.36-40
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• 2003

근래에 들어 질병으로 인하여 의사표현이 곤란한 환자에게 뇌파에 기초한 BCI(Brain Computer Interface)와 같은 새로운 인터페이스를 제공하고자 하는 연구가 활발히 진행되고 있다. BCI를 위한 기초 연구로서 특정 자극에 대해 유발되는 뇌파의 측정과 분석은 BCI를 위한 뇌파의 패턴과 인터페이스의 설계에 중요한 역할을 한다. 이 연구의 목적은 시청각 자극 인가후 피험자의 반응 시간을 측정하는 시스템을 EEG와 같은 생체 신호 계측 시스템과 연동이 가능한 형태로 개발하는 것이다. 제안된 시스템은 기능적으로 자극 신호 발생부, 반응시간 측정부, 유발뇌파 측정부, 동기신호발생부로 나뉘어진다. 자극신호 발생부는 실험에 이용되는 자극신호를 제작하는 부분으로서 Flash를 사용하여 구현하였다. 반응시간 측정부는 문제에 대한 답 선택 요청시각으로부터 피험자의 반응까지의 시간을 측정하는 부분으로서 마이크로 컴퓨터(80C31)를 이용하여 구현하였다. 우발뇌파 측정부는 시판용 하드웨어와 소프트웨어를 그대로 사용하였다. 동기신호 발생부는 전체 시스템의 동기를 맞추기 위한 신호를 발생하는 부분으로서 문제제시, 답요구와 동기한 화면상의 명암 신호와 이를 검출하는 광센서로 구성하였다. 본 논문에서 제시한 방법에서는 기존의 유발진위 측정 및 자극시스템에 특정 모듈(반응시간 측정 장치, 동기신호 발생장치)만을 추가하여 실험자의 의도에 맞는 시스템을 설계할 수 있어 유발 뇌파 및 반응시간 측정을 필요로 하는 연구를 가속화 할 것이 기대된다.