5 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…