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Paul Sajda
Researcher at Columbia University
Publications - 261
Citations - 9050
Paul Sajda is an academic researcher from Columbia University. The author has contributed to research in topics: Electroencephalography & EEG-fMRI. The author has an hindex of 45, co-authored 243 publications receiving 8015 citations. Previous affiliations of Paul Sajda include United States Army Research Laboratory & Sarnoff Corporation.
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Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision
TL;DR: It is shown how ‘corticaltriaging’ improves image search over a strictly behavioral response and can be utilized to construct a novel type of brain-computer interface, which is term cortically-coupled computer vision.
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Automated tissue segmentation and blind recovery of 1H MRS imaging spectral patterns of normal and diseased human brain
TL;DR: The results show the ability of cNMF with iterative data selection to automatically and simultaneously recover tissue‐specific spectral patterns and achieve segmentation of normal and diseased human brain tissue, concomitant with simplification of information content.
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Fusing Multiple Neuroimaging Modalities to Assess Group Differences in Perception–Action Coupling
Jordan Muraskin,Jason Sherwin,Gregory Lieberman,Javier O. Garcia,Timothy Verstynen,Jean M. Vettel,Paul Sajda +6 more
TL;DR: This novel method illustrates how multimodal neuroimaging can provide specific macroscale insights into the functional and structural correlates of expertise development.
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Interplay between components of pupil-linked phasic arousal and its role in driving behavioral choice in Go/No-Go perceptual decision-making.
TL;DR: It is found that phasic arousal evoked by stimulus encoding was larger for the Go stimulus than the No-Go stimulus, and Drift diffusion modeling revealed that the four model parameters were better accounted for when phAsic arousal elicited by both stimulus encoding and decision formation was considered.
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Where Is the User in Multimedia Retrieval
TL;DR: A panel discussion explored this intriguing question: Where is the user in multimedia retrieval, and suggested methods to bring the data and the user back together.