• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.221-229
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• 2004

The Continuous Wavelets Transform project signal f(t) to "Time-scale"plan utilizing the time varied function which called "wavelets". This Transformation permit to analyze scale time dependence of signal f(t) thus the local or global scale properties can be extracted. Moreover, the signal f(t) can be reconstructed stably by utilizing the Inverse Continuous Wavelets Transform. In this paper, the EEG signal is analyzed by wavelets coherence method and the De-noising procedure is represented.

• 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 Institute of Intelligent Systems
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• v.12 no.4
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• pp.366-372
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• 2002

EEG is the electrical signal, which is occurred during information processing in the brain. These EEG signal are measured by non-invasive method. EEG has many useful information for brain activity, but artifacts which are included in EEG prevents EEG analysis, so many efforts are devoted to remove these artifacts in EEG. However, this study is going to analysis the feature of the EEG mixed with artifacts in forward-looking way, by using this way, we have found the possibility that is actually applicable to system such as control system. We have made feature difference after the linear as well as nonlinear analysis regarding EEG including typical artifacts, eye-blinking, eye rolling, muscle, and so forth.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.63-69
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• 2019

In this study, bispectrum analysis method introduced to reduce variability of SEF(spectral edge frequency) and MF(median frequency), which are the anesthetic depth indexes extracted by EEG spectral analysis. Bispectrum analysis is an analytical method that can confirm the nonlinearity of EEG. Signal measurement and analysis in the surgical environment should take into consideration various external artifact factors. Bispectrum analysis can confirm the presence of externally introduced artifacts, thereby effectively eliminating artifacts that affect the EEG signal. By applying bispectrum parameters, real-time variability of the anesthetic depth parameters SEF, MF could be reduced. Elimination of variability makes it possible to use SEF, MF as a real-time index during surgery.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.7-12
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• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.509-513
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• 1992

This paper investigates the EEG waveform distortions caused by the transient responses of various types of signal conditioning filters, which are generally introduced for automated analysis of EEG. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions. The filter distortion effects are also illustrated with the experiments on real EEG signals.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.27-32
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• 2011

This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.

• Journal of IKEEE
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• v.23 no.2
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• pp.727-734
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• 2019

This paper confirms whether the movement or specific operation of the muscles in the process of transferring a person from the brain can find a signal showing an essential feature of a certain part of the brain. As a rule, the occurrence of EEG(Electroencephalogram) changes when a signal is received from a specific action or from an induced action. These signals are very vague and difficult to distinguish from the naked eye. Therefore, it is necessary to define a signal for analysis before classification. The EEG form can be divided into the alpha, beta, delta, theta and gamma regions in the frequency ranges. The specific size of these signals does not reflect the exact behavior or intention, since the band or energy difference of the activated frequencies varies depending on the EEG measurement domain. However, if different actions are performed in a specific method, it is possible to classify the movement based on EEG activity and to determine the EEG tendency affecting the movement. Therefore, in this article, we first study the EEG expression pattern based on the activation of the left and right biceps EMG, and then we determine whether there is a significant difference between the EEG due to the activation of the left and right muscles through EEG. If we can find the EEG classification criteria in accordance with the EMG activation, it can help to understand the form of the transmitted signal in the process of transmitting signals from the brain to each muscle. In addition, we can use a lot of unknown EEG information through more complex types of brain signal generation in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1277-1282
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• 2014

In this paper, we aimed to analysis EEG signals related to concentration of adolescents using letter visual stimulus to induce the concentration. The visual stimulus tasks were searching errors of propositional particle in several sentences. In the EEG signals, we specially focussed on SMR waves and mid-beta waves according to the results of a preceding research. Therefore we presented position of channel and frequency band of mid-beta significantly related to the concentration waves as the experimental results.