5 citations · 6 across the 8 of their papers we have counts for
8 papers
MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery
Reese Kneeland, Cesar Kadir Torrico Villanueva, Jordyn Ojeda +4
To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to generalize to internally gene…
ENIGMA: EEG-to-Image in 15 Minutes Using Less Than 1% of the Parameters
Reese Kneeland, Wangshu Jiang, Ugo Bruzadin Nunes +3
To be practical for real-life applications, models for brain-computer interfaces must be easily and quickly deployable on new subjects, effective on affordable scanning hardware, a…
Alljoined-1.6M: A Million-Trial EEG-Image Dataset for Evaluating Affordable Brain-Computer Interfaces
Jonathan Xu, Ugo Bruzadin Nunes, Wangshu Jiang +5
We present a new large-scale electroencephalography (EEG) dataset as part of the THINGS initiative, comprising over 1.6 million visual stimulus trials collected from 20 participant…
NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
Reese Kneeland, Paul S. Scotti, Ghislain St-Yves +3
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI…
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Paul S. Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva +8
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are tra…
Brain-optimized inference improves reconstructions of fMRI brain activity
Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves +1
The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the p…
Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity
Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves +1
Two recent developments have accelerated progress in image reconstruction from human brain activity: large datasets that offer samples of brain activity in response to many thousan…
Reconstructing seen images from human brain activity via guided stochastic search
Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves +1
Visual reconstruction algorithms are an interpretive tool that map brain activity to pixels. Past reconstruction algorithms employed brute-force search through a massive library to…