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%2C 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%2C there remains a substantial demand for high-quality%2C long-duration%2C 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 fMRI scans. In response to these constraints%2C we propose a novel framework that generates enhanced 3T fMRI data through an unsupervised Generative Adversarial Network (GAN)%2C leveraging unpaired training across two distinct fMRI datasets in 7T and 3T%2C respectively. This approach aims to overcome the limitations of the scarcity of high-quality 7-Tesla data and the challenges associated with brief and low-quality scans in 3-Tesla experiments. In this paper%2C we demonstrate the reconstruction capabilities of the enhanced 3T fMRI data%2C highlighting its proficiency in generating superior input visual images compared to data-intensive methods trained and tested on a single subject.?quality=80&w=800)
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