S
Scott Makeig
Researcher at University of California, San Diego
Publications - 310
Citations - 57153
Scott Makeig 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 83, co-authored 304 publications receiving 49206 citations. Previous affiliations of Scott Makeig include Boston Children's Hospital & University of California.
Papers
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Journal ArticleDOI
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
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
Analysis of fMRI data by blind separation into independent spatial components
Martin J. McKeown,Scott Makeig,Greg G. Brown,Tzyy-Ping Jung,Sandra S. Kindermann,Anthony J. Bell,Terrence J. Sejnowski,Terrence J. Sejnowski +7 more
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
Scott Makeig,Marissa Westerfield,Tzyy-Ping Jung,S. Enghoff,Jeanne Townsend,Eric Courchesne,Terrence J. Sejnowski +6 more
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.