Scott Makeig

Affiliations: 
University of California, San Diego, La Jolla, CA 
Area:
Systems
Website:
http://sccn.ucsd.edu/~scott/
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"Scott Makeig"
Mean distance: 12.73 (cluster 29)
 
SNBCP

Collaborators

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Wolfgang Einhauser collaborator 2005- Caltech
Daniel P. Ferris collaborator 2008-2014 UCSD
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Publications

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Wagner J, Martinez-Cancino R, Makeig S. (2019) Trial-by-trial source-resolved EEG responses to gait task challenges predict subsequent step adaptation. Neuroimage
Perez VB, Miyakoshi M, Makeig SD, et al. (2019) Mismatch negativity reveals plasticity in cortical dynamics after 1-hour of auditory training exercises. International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
Pion-Tonachini L, Kreutz-Delgado K, Makeig S. (2019) ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. Neuroimage
Marini F, Lee C, Wagner J, et al. (2019) A comparative evaluation of signal quality between research-grade and wireless dry-electrode mobile EEG systems. Journal of Neural Engineering
Artoni F, Delorme A, Makeig S. (2019) A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines. Data in Brief. 22: 787-793
Pion-Tonachini L, Hsu SH, Chang CY, et al. (2018) Online Automatic Artifact Rejection using the Real-time EEG Source-mapping Toolbox (REST). Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2018: 106-109
Martinez-Cancino R, Heng J, Delorme A, et al. (2018) Measuring transient phase-amplitude coupling using local mutual information. Neuroimage
Hsu SH, Pion-Tonachini L, Palmer J, et al. (2018) Modeling brain dynamic state changes with adaptive mixture independent component analysis. Neuroimage
Tanaka H, Miyakoshi M, Makeig S. (2018) Dynamics of directional tuning and reference frames in humans: A high-density EEG study. Scientific Reports. 8: 8205
Artoni F, Delorme A, Makeig S. (2018) Applying dimension reduction to EEG data by principal component analysis reduces the quality of its subsequent independent component decomposition. Neuroimage
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