지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.
목차
등록된 정보가 없습니다.
참고문헌 (16)
참고문헌 신청
A. Hyv¨arinen A / 1997 / A fast fixed-point algorithm for independent component analysis / Neural Computation 9 : 483 ~ 492
A. J. Bell / 1995 / An information-maximization approach to blind separation and blind deconvolution / Neural Computation 7 : 129 ~ 1159
B. B. Biswal / 1999 / Blind Source Separation of Multiple Signal Sources of FMRI Data Sets Using lndependent Component Analysis / J. Comput. Assist. Tomogr 23 : 265 ~ 271
E. Seifritz / 2002 / Spatiote- mporal pattern of neural processing in the human auditory cortex / Science 297 (5587) : 1706 ~ 1708
J. -W. Jeong / 2004 / Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG / International Journal of Im
A. J. Bell / 1995 / An information-maximization approach to blind separation and blind deconvolution / Neural Computation 7 : 129 ~ 1159
B. B. Biswal / 1999 / Blind Source Separation of Multiple Signal Sources of FMRI Data Sets Using lndependent Component Analysis / J. Comput. Assist. Tomogr 23 : 265 ~ 271
E. Seifritz / 2002 / Spatiote- mporal pattern of neural processing in the human auditory cortex / Science 297 (5587) : 1706 ~ 1708
J. -W. Jeong / 2004 / Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG / International Journal of Im
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