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Tzyy-Ping Jung

Researcher at University of California, San Diego

Publications -  384
Citations -  33127

Tzyy-Ping Jung is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Electroencephalography & Independent component analysis. The author has an hindex of 68, co-authored 361 publications receiving 28290 citations. Previous affiliations of Tzyy-Ping Jung include University of California, Berkeley & University System of Taiwan.

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Journal ArticleDOI

Removing electroencephalographic artifacts by blind source separation.

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

Analysis of fMRI data by blind separation into independent spatial components

TL;DR: This work decomposed eight fMRI data sets from 4 normal subjects performing Stroop color‐naming, the Brown and Peterson word/number task, and control tasks into spatially independent components, and found the ICA algorithm was superior to principal component analysis (PCA) in determining the spatial and temporal extent of task‐related activation.
Proceedings Article

Independent Component Analysis of Electroencephalographic Data

TL;DR: First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show that ICA training is insensitive to different random seeds and ICA may be used to segregate obvious artifactual EEG components from other sources.
Journal ArticleDOI

Dynamic Brain Sources of Visual Evoked Responses

TL;DR: It is shown that nontarget event-related potentials were mainly generated by partial stimulus-induced phase resetting of multiple electroencephalographic processes in a human visual selective attention task.
Journal ArticleDOI

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

TL;DR: Results show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings, and the results compare favorably to those obtained using rejection or regression methods.