• Title/Summary/Keyword: EEG Signal

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Development of Mirror Neuron System-based BCI System using Steady-State Visually Evoked Potentials (정상상태시각유발전위를 이용한 Mirror Neuron System 기반 BCI 시스템 개발)

  • Lee, Sang-Kyung;Kim, Jun-Yeup;Park, Seung-Min;Ko, Kwang-Enu;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.1
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    • pp.62-68
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    • 2012
  • Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user's inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.

Real Time Drowsiness Detection by a WSN based Wearable ECG Measurement System

  • Takalokastari, Tiina;Jung, Sang-Joong;Lee, Duk-Dong;Chung, Wan-Young
    • Journal of Sensor Science and Technology
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    • v.20 no.6
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    • pp.382-387
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    • 2011
  • Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

The Design of Feature Selection Classifier based on Physiological Signal for Emotion Detection (감성판별을 위한 생체신호기반 특징선택 분류기 설계)

  • Lee, JeeEun;Yoo, Sun K.
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.11
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    • pp.206-216
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    • 2013
  • The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

The Optimization of Hybrid BCI Systems based on Blind Source Separation in Single Channel (단일 채널에서 블라인드 음원분리를 통한 하이브리드 BCI시스템 최적화)

  • Yang, Da-Lin;Nguyen, Trung-Hau;Kim, Jong-Jin;Chung, Wan-Young
    • Journal of the Institute of Convergence Signal Processing
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    • v.19 no.1
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    • pp.7-13
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    • 2018
  • In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

A GAUSSIAN SMOOTHING ALGORITHM TO GENERATE TREND CURVES

  • Moon, Byung-Soo
    • Journal of applied mathematics & informatics
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    • v.8 no.3
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    • pp.731-742
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    • 2001
  • A Gaussian smoothing algorithm obtained from a cascade of convolutions with a seven-point kernel is described. We prove that the change of local sums after applying our algorithm to sinusoidal signals is reduced to about two thirds of the change by the binomial coefficients. Hence, our seven point kernel is better than the binomial coefficients when trend curves are needed to be generated. We also prove that if our Gaussian convolution is applied to sinusoidal functions, the amplitude of higher frequencies reduces faster than the lower frequencies and hence that it is a low pass filter.

The Effect of Finite-bit Approximated Twiddle Coefficients in the SDFT Spectral Analysis (SDFT 스펙트럼 해석 시 계수근사에 따른 오차영향 해석)

  • 김재화;장태규
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.36S no.5
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    • pp.96-103
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    • 1999
  • 본 논문에서는 sliding-DFT(SDFT)를 계수의 유한 비트 근사구현에 기초하여 실시간 구현하는 기법을 제시하고, 이의 오차영향을 해석하였다. 오차의 영향을 오차전력과 신호전력비율(noise-to-signal power ratio : NSR)로 하여 이를 해석적으로 유도하였다. 가우스 렌덤신호 및 사람의 수면 EEG 신호를 대상으로 수행한 시뮬레이션 결과가 해석식과 잘 일치하는 것을 보임으로써 본 연구에서 얻은 해석식을 확인하였다.

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A study about setting optimum Intensity on sensing of light by analysing human electrical signal (인체 전기 신호 해석을 통한 광인식시의 최적 광량 설정에 관한 연구)

  • Jeon, Yun-Jeong;Park, Hyung-Jun;Yoon, Yang-Woong
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.3222-3226
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    • 2000
  • In this study, the variations of human physiological signals(EEG and ERG) were measured on a various optic stimulation. From the analysis of the physiological signals, it was cleared that the optimum intensity of light exits at its sensing.

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A Reconfigurable 4th Order ΣΔ Modulator with a KT/C Noise Reduction Circuit

  • Yang, Su-Hun;Seong, Jae-Hyeon;Yoon, Kwang-Sub
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.17 no.2
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    • pp.294-301
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    • 2017
  • This paper presents a low power ${\Sigma}{\Delta}$ modulator for an implantable chip to acquire a bio-signal such as EEG, DBS, and EMG. In order to reduce a power consumption of the proposed fourth order modulator, two op-amps utilized for the first two integrators are reconfigured to drive the second two integrators. The KT/C noise reduction circuit in the first two integrators is employed to enhance SNR of the modulator. The proposed circuit was fabricated in a 0.18 um CMOS n-well 1 poly 6 metal process with the active chip core area of $900um{\times}800um$ and the power consumption of 830 uW. Measurement results were demonstrated to be SNDR of 76 dB, DR of 77 dB, ENOB of 12.3 bit at the input frequency of 250 Hz and the clock frequency of 256 kHz. FOM1 and FOM2 were measured to be 41 pJ/step and 142.4 dB, respectively.

Signal Processing and Data Management in SiMACS (SiMACS에서의 생체신호처리 및 데이터관리)

  • Suh, J.J.;Kim, J.J.;Lee, S.B.;Park, S.H.;Woo, E.J.
    • Proceedings of the KOSOMBE Conference
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    • v.1994 no.05
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    • pp.57-59
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    • 1994
  • In this paper, we present the software part of the intelligent data processing unit (IDPU), which plays an important role in SiMACS. The software system processes ECG, EEG, EMG, blood pressure, respiration, temperature signals, and extracts some information about patient conditions. It displays the patient condition information and the signal data synchronously, and manages them together with other patient personal data in a network-based client/server environment. The software system is designed in an object-oriented paradigm, and implemented in C++ as a window-based application program.

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The Development of Low-noise EEG Preamplifier (저잡음 뇌파 전치 증폭기의 개발)

  • Yoo, S.K.;Kim, N.H.;Kim, S.H.;Song, J.S.;Ahn, C.B.
    • Proceedings of the KOSOMBE Conference
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    • v.1995 no.05
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    • pp.68-70
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    • 1995
  • A low-noise pre-amplifier is developed for use in Topographic Brain Mapping system. It consists of signal generator, signal amplifier with a impedance converter, shield driver, body driver, differential amplifier, and isolation amplifier. Pre-amplifier circuit is designed with the concept of isolation and active body and shield driver. This amplifier shows the good noise behavior, high CMRR, high input impedance, low leakage current and high IMRR.

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