Human-Aligned Image Models Improve Visual Decoding from the Brain
Nona Rajabi, Antonio H. Ribeiro, Miguel Vasco, Farzaneh Taleb, Mårten Björkman, Danica Kragic
Abstract
Decoding visual images from brain activity has significant potential for advancing brain-computer interaction and enhancing the understanding of human perception. Recent approaches align the representation spaces of images and brain activity to enable visual decoding. In this paper, we introduce the use of human-aligned image encoders to map brain signals to images. We hypothesize that these models more effectively capture perceptual attributes associated with the rapid visual stimuli presentations commonly used in visual brain data recording experiments. Our empirical results support this hypothesis, demonstrating that this simple modification improves image retrieval accuracy by up to 21\% compared to state-of-the-art methods. Comprehensive experiments confirm consistent performance improvements across diverse EEG architectures, image encoders, alignment methods, participants, and brain imaging modalities.
BibTeX
@inproceedings{
rajabi2025humanaligned,
title={Human-Aligned Image Models Improve Visual Decoding from the Brain},
author={Nona Rajabi and Antonio H. Ribeiro and Miguel Vasco and Farzaneh Taleb and M{\r{a}}rten Bj{\"o}rkman and Danica Kragic},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=i6uxIAAMje}
}