T
Te-Won Lee
Researcher at Qualcomm
Publications - 172
Citations - 16820
Te-Won Lee is an academic researcher from Qualcomm. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 54, co-authored 172 publications receiving 15910 citations. Previous affiliations of Te-Won Lee include University of California & University of California, Berkeley.
Papers
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Journal ArticleDOI
Removing electroencephalographic artifacts by blind source separation.
Tzyy-Ping Jung,Tzyy-Ping Jung,Scott Makeig,Colin Humphries,Te-Won Lee,Te-Won Lee,Martin J. McKeown,Vicente J. Iragui,Terrence J. Sejnowski,Terrence J. Sejnowski +9 more
TL;DR: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Journal ArticleDOI
Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources
TL;DR: An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions and is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
Journal ArticleDOI
Dictionary learning algorithms for sparse representation
Kenneth Kreutz-Delgado,Joseph F. Murray,Bhaskar D. Rao,Kjersti Engan,Te-Won Lee,Terrence J. Sejnowski +5 more
TL;DR: Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave negative log priors, showing improved performance over other independent component analysis methods.
Book
Independent Component Analysis: Theory and Applications
TL;DR: This work presents a Unifying Information-Theoretic Framework for ICA, a novel and scalable framework for independent component analysis that combines supervised and unsupervised classification with ICA Mixture Models.
Journal ArticleDOI
Imaging brain dynamics using independent component analysis
Tzyy-Ping Jung,Scott Makeig,Martin J. McKeown,Anthony J. Bell,Te-Won Lee,Terrence J. Sejnowski +5 more
TL;DR: The assumptions underlying ICA are outlined and its application to a variety of electrical and hemodynamic recordings from the human brain is demonstrated.