• Title/Summary/Keyword: Independent component analysis(ICA)

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An Extensive Analysis of High-density Electroencephalogram during Semantic Decision of Visually Presented Words

  • Kim, Kyung-Hwan;Kim, Ja-Hyun
    • Journal of Biomedical Engineering Research
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    • v.27 no.4
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    • pp.170-179
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    • 2006
  • The purpose of this study was to investigate the spatiotemporal cortical activation pattern and functional connectivity during visual perception of words. 61 channel recordings of electroencephalogram were obtained from 15 subjects while they were judging the meaning of Korean, English, and Chinese words with concrete meanings. We examined event-related potentials (ERP) and applied independent component analysis (ICA) to find and separate simultaneously activated neural sources. Spectral analysis was also performed to investigate the gamma-band activity (GBA, 30-50 Hz) which is known to reflect feature binding. Five significant ERP components were identified and left hemispheric dominance was observed for most sites. Meaningful differences of amplitudes and latencies among languages were observed. It seemed that familiarity with each language and orthographic characteristics affected the characteristics of ERP components. ICA helped confirm several prominent sources corresponding to some ERP components. The results of spectral and time-frequency analyses showed distinct GBAs at prefrontal, frontal, and temporal sites. The GBAs at prefrontal and temporal sites were significantly correlated with the LPC amplitude and response time. The differences in spatiotemporal patterns of GBA among languages were not prominent compared to the inter-individual differences. The gamma-band coherence revealed short-range connectivity within frontal region and long-range connectivity between frontal, posterior, and temporal sites.

Speaker Adaptation Using ICA-Based Feature Transformation

  • Jung, Ho-Young;Park, Man-Soo;Kim, Hoi-Rin;Hahn, Min-Soo
    • ETRI Journal
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    • v.24 no.6
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    • pp.469-472
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    • 2002
  • Speaker adaptation techniques are generally used to reduce speaker differences in speech recognition. In this work, we focus on the features fitted to a linear regression-based speaker adaptation. These are obtained by feature transformation based on independent component analysis (ICA), and the feature transformation matrices are estimated from the training data and adaptation data. Since the adaptation data is not sufficient to reliably estimate the ICA-based feature transformation matrix, it is necessary to adjust the ICA-based feature transformation matrix estimated from a new speaker utterance. To cope with this problem, we propose a smoothing method through a linear interpolation between the speaker-independent (SI) feature transformation matrix and the speaker-dependent (SD) feature transformation matrix. From our experiments, we observed that the proposed method is more effective in the mismatched case. In the mismatched case, the adaptation performance is improved because the smoothed feature transformation matrix makes speaker adaptation using noisy speech more robust.

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Independent Component Analysis for the Blind Separation Of Spatially Independent Components From ${H_2}^{15}O$ Dynamic Myocardial Positron Emission Tomography

  • Lee, Jae-Sung;Ahn, Ji-Young;Lee, Dong-Soo;Park, Kwang-Suk
    • Proceedings of the IEEK Conference
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    • 2000.06e
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    • pp.134-137
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    • 2000
  • We applied the ICA method to separate the ventricle and tissue components and to extract left ventricular input function from the H$_2$$^{15}$ O myocardial PET under the assumption that the elementary activities of ventricular pools and myocardium were spatially independent, and that the mixture of them composed dynamic PET frames. ICA-generated left ventricular input functions were compared with the ROI-generated ones, and also with the invasively derived arterial blood samples. Moreover, the rMBF calculated with the ICA-generated input functions and single compartment model was correlated with the results obtained with the radiolabeled microspheres.

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Blind Image Separation with Neural Learning Based on Information Theory and Higher-order Statistics (신경회로망 ICA를 이용한 혼합영상신호의 분리)

  • Cho, Hyun-Cheol;Lee, Kwon-Soon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.8
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    • pp.1454-1463
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    • 2008
  • Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to solve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.

Suppressing Artefacts in the ECG by Independent Component Analysis (독립성분 분석기법에 의한 심전도 신호의 왜곡 보정)

  • Kim, Jeong-Hwan;Kim, Kyeong-Seop;Kim, Hyun-Tae;Lee, Jeong-Whan
    • 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 Suggestion of LINF Algorithm for a Real-time Face Recognition System (실시간 얼굴인식 시스템을 위한 새로운 LINF 알고리즘의 제안)

  • Jang Hye-Kyoung;Kang Dae-Seong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.4 s.304
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    • pp.79-86
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    • 2005
  • In this paper, we propose a new LINF(Linear Independent Non-negative Factorization) algorithm for real-time face recognition systea This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction Part we applied subtraction image, the detection of eye and mouth region , and normalization method, and then in the face recognition Part we used LINF in extracted face candidate region images. The existing recognition system using only PCA(Principal Component Analysis) showed low recognition rates, and it was hard in the recognition system using only LDA(Linear Discriminants Analysis) to apply LDA directly when the training set is small. To overcome these shortcomings, we reduced dimension as the matrix that had non-negative value to be different from former eigenfaces and then applied LDA to the matrix in the proposed system We have experimented using self-organized DAIJFace database and ORL database offered by AT(')T laboratory in Cambridge, U.K. to evaluate the performance of the proposed system. The experimental results showed that the proposed method outperformed PCA, LDA, ICA(Independent Component Analysis) and PLMA(PCA-based LDA mixture algorithm) method within the framework of recognition accuracy.

Comparison of Independent Component Analysis and Blind Source Separation Algorithms for Noisy Data (잡음환경에서 독립성분 분석과 암묵신호분리 알고리즘의 성능비교)

  • O, Sang-Hun;Cichocki, Andrzej;Choe, Seung-Jin;Lee, Su-Yeong
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.39 no.2
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    • pp.10-20
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    • 2002
  • Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.

An Improved EEG Signal Classification Using Neural Network with the Consequence of ICA and STFT

  • Sivasankari, K.;Thanushkodi, K.
    • 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.

Robust Feature Parameter for Implementation of Speech Recognizer Using Support Vector Machines (SVM음성인식기 구현을 위한 강인한 특징 파라메터)

  • 김창근;박정원;허강인
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.3
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    • pp.195-200
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    • 2004
  • In this paper we propose effective speech recognizer through two recognition experiments. In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition performance of HMM and SVM at training data number and investigate recognition performance of each feature parameter while changing feature space of MFCC using Independent Component Analysis(ICA) and Principal Component Analysis(PCA). As a result of experiment, recognition performance of SVM is better than 1:.um under few training data number, and feature parameter by ICA showed the highest recognition performance because of superior linear classification.

Comparison of several criteria for ordering independent components (독립성분의 순서화 방법 비교)

  • Choi, Eunbin;Cho, Sulim;Park, Mira
    • The Korean Journal of Applied Statistics
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    • v.30 no.6
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    • pp.889-899
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    • 2017
  • Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.