Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning

arXiv:2404.05107v1 Announce Type: new
Abstract: The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechanisms of the human visual system. Despite the significant strides made by deep learning methods in improving the quality and interpretability of visual reconstruction, there remains a substantial demand for high-quality, long-duration, subject-specific 7-Tesla fMRI experiments. The challenge arises in integrating diverse smaller 3-Tesla datasets or accommodating new subjects with brief and low-quality …

abstract arxiv brain brain activity cs.cv data deep learning fmri functional human images imaging improving inputs interpretability quality through type unsupervised unsupervised learning visual