• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.515-520
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• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.825-832
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• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.351-359
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• 2003

In this paper, we proposed a method for acoustic echo cancellation based on independent component analysis. When the large acoustic noise is picked up by the microphone, the performance of echo cancellation decreased. We used two microphones that received echo signal which is linearly mixed with the noise, then separated the echo signals from the received signals with independent component analysis algorithm. The separated echo signal is used for the reference signal of adaptive algorithm which leads to better performance of the echo cancellation. Computer simulation results show the validity of the proposed method.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.71-78
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• 2016

PPG signal measured by pulse oximeter can measure pulse and the oxygen saturation of arterial blood. But the PPG signal is distorted by finger movement or other movement in the body. To detect pulse from the PPG signal with motion artifact, we use band pass filter(BPF), Independent component analysis(ICA) and nonlinear autocorrelation(NAC). BPF is used to remove DC component and high frequency noise in the PPG signal with motion artifacts. ICA is used to separate pulse signal and motion artifact. However, pulse signal separated by ICA have no choice but to accompany signal distortion because pulse signal and motion artifact are not completely independent. So, we use nonlinear autocorrelation to emphasize the pure pulse signal from the distorted signal.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.485-487
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• 2003

Independent Component Analysis (ICA) is used to generate ICA filter for computing feature vector for image window. Filters that have high discrimination power are selected to classify image from these ICA filters. Proposed classification algorithm is based on probability distribution of feature vector.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.270-277
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• 2010

In this paper, we proposed an algorithm that improves tone quality of noisy audio signals by using ICA(Independent Component Analysis) algorithm and perceptual filters. Many algorithms have been proposed to eliminate the noise from the audio signals, such as spectral subtraction method, perceptual filter, etc. The perceptual filter uses a noise that is acquired from silent ranges in the input signal. In this case, the improvement rate of tone quality decreases if the noise energy is changed by the environmental variation in a signal frame. But the proposed method estimates a noise that is changed at each frame using ICA algorithm. The estimated noise is applied to perceptual filter. To show the performance of the proposed algorithm, several tests are performed to various input signals. With the proposed algorithm, we could confirm the enhancement of tone quality in terms of segmental SNR (SSNR), noise-to-mask ratio (NMR) and Degradation Category Rating (DCR) test.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.305-309
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• 2000

In this paper, we presents a new methodology for face recognition after analysing conventional ICA(Independent Component Analysis) based approach. In the literature we found that ICA based methods have followed the same procedure without any exception, first PCA(Principal Component Analysis) has been used for feature extraction, next ICA learning method has been applied for feature enhancement in the reduced dimension. However, it is contradiction that features are extracted using higher order moments depend on variance, the second order statistics. It is not considered that a necessary component can be located in the discarded feature space. In the new methodology, features are extracted using the magnitude of kurtosis(4-th order central moment or cumulant). This corresponds to the PCA based feature extraction using eigenvalue(2nd order central moment or variance). The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. ICA methodology is analysed using SVD(Singular Value Decomposition). PCA does whitening and noise reduction. ICA performs the feature extraction. Simulation results show the effectiveness of the methodology compared to the conventional ICA approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.691-697
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• 2013

Recently, u-healthcare device is developed and commercialized for healthcare management and emergency medical. The kinds of the measurable biomedical signals on the device are electrocardiogram, skin temperature, pulse oxygen, heart rate, respiration, etc. Specially, the photoplethysmograph(PPG) signal of these signals is the important signal in measuring oxygen, heart rate and peripheral vascular compliance. The accuracy of PPG signal reduce from influence of the motion artifacts that generated from the movements of user or patient. Therefore, this study suggests a new method to remove the motion artifact that is using optical power modulation and ICA(Independent Component Analysis). For analyzing the proposed method, we used variety of noises made by artificially. In the results of experiments, the proposed method showed good performances than an adaptive filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1431-1437
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• 2016

In mobile healthcare device, when to measure the heart rate by using the PPG signal, its performance is reduced according to the motion artifacts that is the movement of user. This is because the frequency range of motion (0.01-10 Hz) and that of PPG signals overlap. Also, the motion artifacts cannot be rectified by general filters. To solve the problem, this paper proposes a method using filter banks and independent component analysis (ICA). To evaluate the performance of the proposed method, we were artificially applied various movements and compared heart rate errors of the moving average filter and ICA. In the experimental results, heart rate error of the proposed method showed very low than moving average filter and ICA. In this way, it is possible to measure stable heart rate if the proposed method is applied to the healthcare terminal design.

• Progress in Superconductivity
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• v.10 no.2
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• pp.139-143
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• 2009

We have fabricated a low-noise 61-channel axial-type first-order gradiometer system for measuring fetal magnetocardiography(MCG) signals. Superconducting quantum interference device(SQUID) sensor was based on double relaxation oscillation SQUID(DROS) for detecting biomagnetic signal, such as MCG, magnetoencphalogram(MEG) and fetal-MCG. The SQUID sensor detected axial component of fetal MCG signal. The pickup coil of SQUID sensor was wound with 120 ${\mu}m$ NbTi wire on bobbin(20 mm diameter) and was a first-order gradiometer to reject the environment noise. The sensors have low white noise of 3 $fT/Hz^{1/2}$ at 100 Hz on average. The fetal MCG was measured from $24{\sim}36$ weeks fetus in a magnetically shielded room(MSR) with shielding factor of 35 dB at 0.1 Hz and 80 dB at 100 Hz(comparatively mild shielding). The MCG signal contained maternal and fetal MCG. Fetal MCG could be distinguished relatively easily from maternal MCG by using independent component analysis(ICA) filter. In addition, we could observe T peak as well as QRS wave, respectively. It will be useful in detecting fetal cardiac diseases.