• 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.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.221-228
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• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.311-313
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• 1992

Automated analysis of EEG invariably requires the inclusion of a signal conditioning filter. This paper investigates the EEG waveform distortions caused by the transient responses of the various types of signal conditioning filters. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions.

• 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.

• 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.

• Journal of Sensor Science and Technology
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• v.12 no.3
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• pp.121-127
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• 2003

In this study, we carried out a study for implementation of the pre-amplifier and the digital signal processing part for the potable EEG biofeedback system. As we consider characteristics of the EEG signal, we designed the pre-amplifier to obtain the EEG signal to be reduced noise signal. Because the EEG signal include EOG, EMG, ECG signals etc, it is difficult to analyze of the EEG signal. Therefore, we developed DSP board and operation program which was embed the LMS adaptive filter algorithm and operate with the pre-amplifier in the real time. The simulation signal and pure EEG signal is used in the experiment. As the result, we confirmed good efficiency of developed system and possibility of application to the portable EEG biofeedback system.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.145-147
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• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We have developed concentration wireless transmission system by displaying this EEG signal on PDA mobile device. The front head was used for measuring EEG signal and INA128 with TL084 and analog elements was used for measuring EEG signal, amplifying and filtering the signal. Measured analog EEG signals changed into digital signals by using ADC of PIC24FJ192 with 10bit resolution and 500Ks/s sampling rate. So The changed digital signals have transmitted to the PDA by using bluetooth. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the transferred EEG signal. As a result, $\alpha$ wave, $\beta$ wave, $\theta$ wave and $\delta$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into PDA by wireless module and displaying.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.2
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• pp.297-309
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• 2019

This study investigated EEG signal, subjective fragrance sensation, and the preference of differently colored cotton knit treated with Citrus unshiu oil containing microcapsules as well as examined their relationships for providing regression models on subjective fragrance preference. Color variables combining 2-level hue (Yellow and Green) and 3-level tone (strong, pale, and grayish) were applied by dyeing prior to microcapsule treatment. We invited 28 female college students aged 20's for EEG signal experiments and subjective fragrance sensations with fragrant knit by rubbing. EEG signals at $mid-{\alpha}$, $fast-{\alpha}$, and $low-{\beta}$ showed significant differences depending on color; Green had more relative power values and grayish tone did more at $low-{\beta}$. Even though subjective sensation showed no significant differences depending on color, some of them such as Fresh, Comfort, and Natural showed significant correlations with EEG signal at $low-{\beta}$, which means that the fragrance sensations of Citrus unshiu fragrance are concerned with attention and alertness for Koreans. Fragrance preference was regressed significantly using some EEG signals and subjective sensation. The results could be utilized to value up fragrant textiles by Citrus unshiu oil.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.301-308
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• 1995

A high-precision pre-amplifier is designed for general use in EEG measurement system. It consists of signal generator, signal amplifier with a impedance converter, shield driver, body driver, differential amplifier, and isolation amplifier. The combination of minimum use of inaccurate passive components and the appropriate matching of each monolithic amplifiers results in good noise behavior, low leakage current, high CMRR, high input impedance, and high IMRR. The performance of EEG pre-amplifier has been verified by showing the typical EEG pattevn of a nomad person through the clinical experiments.