Mining EEG–fMRI using independent component analysis
نویسندگان
چکیده
منابع مشابه
Weather Data Mining Using Independent Component Analysis
In this article, we apply the independent component analysis technique for mining spatio-temporal data. The technique has been applied to mine for patterns in weather data using the North Atlantic Oscillation (NAO) as a specific example. We find that the strongest independent components match the observed synoptic weather patterns corresponding to the NAO. We also validate our results by matchi...
متن کاملMining HIV Dynamics Using Independent Component Analysis
MOTIVATION We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. We show that this technique takes advantage of the ICA power to eliminate the noise generated by artificial interaction of HIV system dynamics. Moreover, the incorporation of the actual laboratory data sets into the a...
متن کاملMining EEG-fMRI using independent component analysis.
Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the brain's response to stimuli, ICA allows the researcher to explore the factors that constitute the data and alleviates the need for explicit spatial and temporal ...
متن کاملMining Text for Word Senses Using Independent Component Analysis
The assumption that the problem of ambiguity in text analysis can only be solved if statistical dependencies of higher than second order are considered leads us to independent component analysis (ICA), a statistical formalism that takes higher-order dependencies into account. By assuming independence, ICA is capable of detecting a set of hidden vectors if only different linear mixtures of these...
متن کاملRank based Least-squares Independent Component Analysis
In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Psychophysiology
سال: 2009
ISSN: 0167-8760
DOI: 10.1016/j.ijpsycho.2008.12.018