• Title, Summary, Keyword: EEG

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EEG Signal Classification based on SVM Algorithm (SVM(Support Vector Machine) 알고리즘 기반의 EEG(Electroencephalogram) 신호 분류)

  • Rhee, Sang-Won;Cho, Han-Jin;Chae, Cheol-Joo
    • Journal of the Korea Convergence Society
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    • v.11 no.2
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    • pp.17-22
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    • 2020
  • In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

Real time automatic EEG report making based on quantitative interpretation of awake EEG

  • Nakamura, Masatoshi;Shibasaki, Hiroshi;Imajoh, Koaru;Ikeda, Akio;Mitsuyasu, Isao
    • 제어로봇시스템학회:학술대회논문집
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    • pp.503-508
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    • 1992
  • A new method for making automatic electroencephalogram(EEG) report based on the automatic quantitative interpretation of awake EEG was developed. We first analysed a. relationship between EEG reports and quantitative EEG interpretation done by a qualified electroencephalographer(EEGer) for 22 subjects. Based on the analysed relationship and usual process of report making by the EEGer, we defined all terminology necessary for EEG report and established rules for EEG report making. By the combined use of the proposed EEG report making and the method for automatic quantitative EEG interpretation presented at '90 KACC, we were able to make the automatic EEG reports which were equivalent to the EEG reports written by the EEGer. As all the procedures were programmed in a personal computer equipped with an AD (analogue-to-digital) converter, the automatic EEG reports were obtained in almost real time in usual actual EEG recording situation with only a few seconds time lag for the analysis in the computer. The proposed report making method and the quantitative EEG interpretation method will be effectively applicable to the clinical use as an assistant tool for physicians.

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Comparison of EEG Characteristics between Dementia Patient and Normal Person Using Frequency Analysis Method (주파수분석법에 의한 치매환자와 정상인의 뇌파특성 비교)

  • Jang, Yun-Seok;Park, Kyu-Chil;Han, Dong-Wook
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.5
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    • pp.595-600
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    • 2014
  • Nowadays our society is rapidly transforming into an aging society. A better understanding of dementia is a high priority in the aging society. Therefore our study is basically aimed at understanding characteristics of EEG signals from dementia patients. Firstly, we analyzed spontaneous EEG signals from normal persons and dementia patients to distinguish their characteristics. The EEG signals are recorded with 16 electrodes and we classified the EEG signals form the signals according to frequency band. To obtain the clean EEG signals, we used cross correlation function between two channels. From the analysis results, we can observe that the EEG characteristics from dementia patients are distinctly different from that from normal persons.

Sleep Onset Period from the EEG Point of View (뇌파 영역에서 수면 발생 과정)

  • Lee, Hyun-Kwon;Park, Doo-Heum
    • Sleep Medicine and Psychophysiology
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    • v.16 no.1
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    • pp.16-21
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    • 2009
  • In accordance with the development of EEG and polysomnography in the field of sleep research, the sleep onset period (SOP) between wakefulness and sleep has been considered an important part for understanding the physiology of sleep. SOP in the transition from wakefulness to sleep is a gradual process integrating various viewpoints such as behavior, EEG, physiology and subjective report. Particularly, based on understanding of EEG changes during sleep, SOP has been regarded as a pattern of topographical change in specific frequency and specific state in EEG. Studies on quantitative EEG (qEEG) and event-related potential (ERP) have suggested that SOP shows the changes of functional coordination at the specific cortical areas in qEEG and the changes of regular patterns in response to environmental stimulation in ERP. The development of sleep EEG and topographic mapping of EEG is expected to integrate various viewpoints of SOP and clarify the neurophysiologic mechanism of SOP further.

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Indoor Environment Control System based EEG Signal and Internet of Things (EEG 신호 및 사물인터넷 기반 실내 환경 제어 시스템)

  • Jeong, Haesung;Lee, Sangmin;Kwon, Jangwoo
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.11 no.1
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    • pp.45-52
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    • 2017
  • EEG signals that are the same as those that have the same disabled people. So, the EEG signals are becoming the next generation. In this paper, we propose an internet of things system that controls the indoor environment using EEG signal. The proposed system consists EEG measurement device, EEG simulation software and indoor environment control device. We use data as EEG signal data on emotional imagination condition in a comfortable state and logical imagination condition in concentrated state. The noise of measured signal is removed by the ICA algorithm and beta waves are extracted from it. then, it goes through learning and test process using SVM. The subjects were trained to improve the EEG signal accuracy through the EEG simulation software and the average accuracy were 87.69%. The EEG signal from the EEG measurement device is transmitted to the EEG simulation software through the serial communication. then the control command is generated by classifying emotional imagination condition and logical imagination condition. The generated control command is transmitted to the indoor environment control device through the Zigbee communication. In case of the emotional imagination condition, the soft lighting and classical music are outputted. In the logical imagination condition, the learning white noise and bright lighting are outputted. The proposed system can be applied to software and device control based BCI.

Automatic interpretation of awaked EEG by using constructive neural networks with forgetting factor

  • Nakamura, Masatoshi;Chen, Yvette;Sugi, Takenao;Ikeda Akio;Shibasaki Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.505-508
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    • 1995
  • The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

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AUTOMATIC INTERPRETATION OF AWAKE EEG;ARTIFICIAL REALIZATION OF HUMAN SKILL

  • Nakamura, Masatoshi;Shibasaki, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.19-23
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    • 1996
  • A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

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User Authentication Method using EEG Signal in FIDO System (FIDO 시스템에서 EEG 신호를 이용한 사용자 인증 방법)

  • Kim, Yong-Ki;Chae, Cheol-Joo;Cho, Han-Jin
    • Journal of the Korea Convergence Society
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    • v.9 no.1
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    • pp.465-471
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    • 2018
  • Recently, biometric technology has begun to be used as a fusion of IT technology and financial system. Using this biometric technology, FIDO(Fast Identity Online) technology, Samsung and Apple started Samsung Pay and Apple Pay service. FIDO authentication technology replaces existing authentication methods such as passwords. Among the biometric technologies, fingerprint recognition technology is attracting attention because it can minimize the device and user rejection at a relatively low price. However, fingerprint information has a limited number of users and it can not be reused if fingerprint information is leaked by an external attacker. Therefore, in this paper, we propose a method to authenticate a user using EEG signal which is one of biometrics technologies. W propose a method to use EEG signal measurement value in FIDO system by using convenience channel by using short channel EEG device. And propose a method to utilize EEG signal when the user recognizes a specific entity by measuring the EEG signal before and after recognizing a specific entity.

Controversies in Usefulness of EEG for Clinical Decision in Epilepsy: Pros. and Cons. (간질 치료에서 뇌파의 임상적 유용성에 관한 논란: 긍정과 부정적 관점에서)

  • Park, Soochul
    • Annals of Clinical Neurophysiology
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    • v.9 no.2
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    • pp.59-62
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    • 2007
  • Electroencephalogram (EEG) is an indispensable tool for diagnosis of epilepsy and is the only assisting barometer of complete remission of epilepsy, which means prolonged, persistent suppression of cortical excitement in epileptic focus in addition to the clinical control of epileptic seizure. The specific morphologies or distribution of epileptic form discharges give us good information for the classification of seizure or epilepsy and epileptic syndromes, which consists of "Pros." in terms of diagnostic approach. In contrast, the EEG as a tool for long-term follow up might be limited due to the various clinical situation of each patient, which consists of "Cons." in terms of the usefulness of EEG for clinical decision. "Cons." aspect of EEG, which clinicians are more frequently coped with than those of "Pros", is an obstacle of utilization of follow up EEG in clinical practice. This is an overview about controversies in usefulness of EEG and the detailed aspects of "Pros." and "Cons." of EEG for clinical decision will be discussed following two articles. We tried to make consensus for the usefulness of EEG especially in the situation of "Cons." with plausible guideline.

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Motor Imagery based Application Control using 2 Channel EEG Sensor (2채널 EEG센서를 활용한 운동 심상기반의 어플리케이션 컨트롤)

  • Lee, Hyeon-Seok;Jiang, Yubing;Chung, Wan-Young
    • Journal of Sensor Science and Technology
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    • v.25 no.4
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    • pp.257-263
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    • 2016
  • Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.