• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.131-133
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• 2000

In this paper, we proposed a new method for localizing the independent sources generating the observed EEG based on independent component analysis (ICA). The performance of the algorithm was tested through computer simulations.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.250-252
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• 2004

In this paper we apply several variations of independent component analysis( ICA) methods, such as spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to yeast cell cycle datasets, and compare their performance in finding components that result in gene clusters coherent with annotations and in extract ins meaningful temporal modes. It turns out that the results of tICA are superior to those of PCA, sICA, and stICA in terms of gene clustering and the temporal modes extracted by stICA highlights particular cellular processes.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.70-76
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• 2012

The independent component analysis (ICA) technique is a source identification method that uses statistical independence to separate source signals from measured signals. It has been successfully applied to various fields such as medical care and communication. In this study, the ICA technique was adopted to analyze the vibration source contribution of plate structures. The theory of the ICA technique is introduced and the procedure of the vibration source contribution analysis based on the ICA technique is proposed. To investigate the applicability of the proposed method to plate structures, numerical examples are presented for a rectangular plate under harmonic force excitations. The results show that the proposed method could become an effective tool for the vibration source contribution analysis of a plate structure.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.371-376
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• 2003

In this paper, we proposes a method for recognizing the facial expressions using ICA(Independent Component Analysis)-factorial representation method. Facial expression recognition consists of two stages. First, a method of Feature extraction transforms the high dimensional face space into a low dimensional feature space using PCA(Principal Component Analysis). And then, the feature vectors are extracted by using ICA-factorial representation method. The second recognition stage is performed by using the Euclidean distance measure based KNN(K-Nearest Neighbor) algorithm. We constructed the facial expression database for six basic expressions(happiness, sadness, angry, surprise, fear, dislike) and obtained a better performance than previous works.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.188-195
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• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

• Annals of Clinical Neurophysiology
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• v.3 no.1
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• pp.26-30
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• 2001

Purpose : Eyeball movement is one of the main artifacts in EEG. A new approach to the removal of these artifacts is presented using independent component analysis(ICA). This technique is a signal-processing algorithm to separate independent sources from unknown mixed signals. This study was performed to show that ICA is a useful method for the separation of EEG components with little data deformity. Methods : 12 sets of 10 sec digital EEG data including eye opening and closure were obtained using international 10~20 system scalp electrodes. ICA with 18 tracings of double banana bipolar montage was performed. Among obtained 18 independent components, two components, which were thought to be eyeball movements were removed. Other 16 components were reconstructed into original bipolar montage. Power spectral analysis of EEGs before and after ICA was done and compared statistically. Total 12 pairs of data were compared by visual inspection and relative power comparison. Results : Waveforms of each pair looked alike by visual inspection. Means of relative power before and after ICA were 29.16% vs. 28.27%, 12.12% vs. 12.41%, 10.55% vs. 10.52%, and 19.33% vs. 18. 33% for alpha, beta, theta, and delta, respectively. These values were statistically same before and after ICA. Conclusions : We found little data deformity after ICA and it was possible to isolate eyeball movements in EEG recordings. Many other components of EEG could be selectively separated using ICA.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.623-629
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• 2004

In this paper, a Efficient method for the mixed image separation is presented using independent component analysis and the new fast expectation-maximization(EM) algorithm. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme in various applications. However, it has been known that ICA does not establish good performance in source separation by itself. So, Innovation process which is one of the methods that were employed in image separation using ICA, which produces improved the mixed image separation. Unfortunately, the innovation process needs long processing time compared with ICA or EM. Thus, in order to overcome this limitation, we proposed new method which combined ICA with the New fast EM algorithm instead of using the innovation process. Proposed method improves the performance and reduces the total processing time for the Image separation. We compared our proposed method with ICA combined with innovation process. The experimental results show the effectiveness of the proposed method by applying it to image separation problems.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.23-28
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• 2002

Among effective feature extraction methods from the small-patched image set, independent component analysis (ICA) is recently well known stochastic manner to find informative basis images. The ICA simultaneously learns both basis images and independent components using high order statistic manners, because that information underlying between pixels are sensitive to high-order statistic models. The topographic ICA model is adapted in our experiment. This paper deals with an unsupervised classification strategies using learned ICA basis images. The experimental result by proposed classification technique shows superior performance than classic texture analysis techniques for the panchromatic KOMPSAT imagery.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.381-384
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• 2006

In this paper, we have proposed an independent component analysis (ICA) technique and a median method based on GBD algorithm which classifies images. The proposed method can measure the distance between images, while it does not lose a portion of the image that changes rapidly. Our simulation results show that it not possible for ICA to recognize the relative discernment between images when K is 7. However, with the median method, it can be possible to such a recognition.