5 citations · 5 across the 5 of their papers we have counts for
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…
Progress Towards Decoding Visual Imagery via fNIRS
Michel Adamic, Wellington Avelino, Anna Brandenberger +14
We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction mo…
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…